Event characterization using hybrid das/dts measurements

ABSTRACT

A method of determining the presence and/or extent of an event comprises determining a plurality of temperature features from a temperature sensing signal, determining one or more frequency domain features from an acoustic signal, and using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features to determine the presence and/or extent of the event at one or more locations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 35 U.S.C. § 371 national stage application ofPCT/EP2020/051817 filed Jan. 24, 2020, entitled “Event CharacterizationUsing Hybrid DAS/DTS Measurements,” which claims priority toPCT/EP2019/078197 filed Oct. 17, 2019, entitled “Fluid InflowCharacterization Using Hybrid DAS/DTS Measurements,” each of which ishereby incorporated herein by reference in its entirety for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

It can be desirable to detect the presence and/or extent of variousevents. For example, to obtain hydrocarbons from subterraneanformations, wellbores are drilled from the surface to access thehydrocarbon-bearing formation. After drilling a wellbore to the desireddepth, a production string is installed in the wellbore to produce thehydrocarbons from one or more production zones of the formation to thesurface. The production of the fluids can be detected at the wellheadbased on total flow of fluid. However, it can be difficult to determinewhere the fluid is inflowing into the wellbore when multiple productionszones are present and an extent of the fluid inflow (e.g., a fluidinflow rate).

BRIEF SUMMARY

In some embodiments, a method of determining the presence and/or extentof an event comprises determining a plurality of temperature featuresfrom a (e.g., a distributed) temperature sensing signal originating,determining one or more frequency domain features from an acousticsignal, and using at least one temperature feature of the plurality oftemperature features and at least one frequency domain feature of theone or more frequency domain features to determine the presence and/orextent of the event at one or more locations.

In some embodiments, a method of determining the presence and/or extentof an event comprises determining a plurality of temperature featuresfrom a temperature sensing signal, determining one or more frequencydomain features from an acoustic signal, and using at least onetemperature feature of the plurality of temperature features and atleast one frequency domain feature of the one or more frequency domainfeatures to determine the presence and/or extent of the event at one ormore locations. The plurality of temperature features comprise at leasttwo of: a depth derivative of temperature with respect to depth, atemperature excursion measurement, a baseline temperature excursion, apeak-to-peak value, an autocorrelation, a Fast Fourier Transform (FFT)of the temperature sensing signal, a Laplace transform of thetemperature sensing signal, a wavelet transform of the temperaturesensing signal and/or of a derivative of the temperature sensing signalwith respect to length (e.g., depth), or a derivative of flowingtemperature with respect to length (depth), as described by Equation(1), a heat loss parameter, a time-depth derivative, or a depth-timederivative.

In some embodiments, a system of determining the presence and/or extentof an event (e.g., within a wellbore) comprises a processor, a memory,and an analysis program stored in the memory. The analysis program isconfigured, when executed on the processor, to receive a (e.g.,distributed) temperature sensing signal and an acoustic signal, whereinthe temperature sensing signal and the acoustic signal originated fromthe event (e.g., within the wellbore), determine a plurality oftemperature features from the temperature sensing signal, determine oneor more frequency domain features from the acoustics signal, anddetermine the presence and/or extent of the event at one or morelocations (e.g., along the wellbore) using at least one temperaturefeature of the plurality of temperature features and at least onefrequency domain feature of the one or more frequency domain features.

Embodiments described herein comprise a combination of features andcharacteristics intended to address various shortcomings associated withcertain prior devices, systems, and methods. The foregoing has outlinedrather broadly the features and technical characteristics of thedisclosed embodiments in order that the detailed description thatfollows may be better understood. The various characteristics andfeatures described above, as well as others, will be readily apparent tothose skilled in the art upon reading the following detaileddescription, and by referring to the accompanying drawings. It should beappreciated that the conception and the specific embodiments disclosedmay be readily utilized as a basis for modifying or designing otherstructures for carrying out the same purposes as the disclosedembodiments. It should also be realized that such equivalentconstructions do not depart from the spirit and scope of the principlesdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of various exemplary embodiments, referencewill now be made to the accompanying drawings in which:

FIG. 1 is a schematic, cross-sectional illustration of a downholewellbore environment according to some embodiments;

FIGS. 2A and 2B are a schematic, cross-sectional views of embodiments ofa well with a wellbore tubular having an optical fiber inserted thereinaccording to some embodiments;

FIG. 3 is a schematic view of an embodiment of a wellbore tubular withfluid inflow and sand ingress according to some embodiments;

FIG. 4 is a flow chart of a method for determining event (e.g., fluidinflow) locations (e.g., within a wellbore) according to someembodiments;

FIG. 5 is a flow diagram of a method of determining an extent of anevent (e.g., a fluid inflow rate of a fluid inflow event) at one or morelocations (e.g., within a wellbore) according to some embodiments;

FIG. 7 schematically illustrates a computer that may be used to carryout various methods according to some embodiments.

DETAILED DESCRIPTION

The following discussion is directed to various exemplary embodiments.However, one of ordinary skill in the art will understand that theexamples disclosed herein have broad application, and that thediscussion of any embodiment is meant only to be exemplary of thatembodiment, and not intended to suggest that the scope of thedisclosure, including the claims, is limited to that embodiment.

The drawing figures are not necessarily to scale. Certain features andcomponents herein may be shown exaggerated in scale or in somewhatschematic form and some details of conventional elements may not beshown in interest of clarity and conciseness.

Unless otherwise specified, any use of any form of the terms “connect,”“engage,” “couple,” “attach,” or any other term describing aninteraction between elements is not meant to limit the interaction todirect interaction between the elements and may also include indirectinteraction between the elements described. In the following discussionand in the claims, the terms “including” and “comprising” are used in anopen-ended fashion, and thus should be interpreted to mean “including,but not limited to . . . ”. Reference to up or down will be made forpurposes of description with “up,” “upper,” “upward,” “upstream,” or“above” meaning toward the surface of the wellbore and with “down,”“lower,” “downward,” “downstream,” or “below” meaning toward theterminal end of the well, regardless of the wellbore orientation.Reference to inner or outer will be made for purposes of descriptionwith “in,” “inner,” or “inward” meaning towards the central longitudinalaxis of the wellbore and/or wellbore tubular, and “out,” “outer,” or“outward” meaning towards the wellbore wall. As used herein, the term“longitudinal” or “longitudinally” refers to an axis substantiallyaligned with the central axis of the wellbore tubular, and “radial” or“radially” refer to a direction perpendicular to the longitudinal axis.The various characteristics mentioned above, as well as other featuresand characteristics described in more detail below, will be readilyapparent to those skilled in the art with the aid of this disclosureupon reading the following detailed description of the embodiments, andby referring to the accompanying drawings.

As utilized herein, a ‘fluid inflow event’ includes fluid inflow (e.g.,any fluid inflow regardless of composition thereof), gas phase inflow,aqueous phase inflow, and/or hydrocarbon phase inflow. The fluid cancomprise other components such as solid particulate matter (e.g., sand,etc.) in some embodiments, as discussed in more detail herein.

Disclosed herein are systems and methods for detecting and/orcharacterizing an event, for example, determining the presence of anevent, such as a fluid inflow event, at one or more locations, and/or anextent of the event, such as inflow quantities (e.g., within asubterranean wellbore). Other events can also be detected such assecurity events, transportation events, geothermal events, carboncapture and CO₂ injection events, facility monitoring events, pipelinemonitoring events, dam monitoring events, and the like. The systemsallow for an identification of the event location(s) as well as theextent (e.g., inflow rates) at those locations using temperaturefeatures derived from temperature measurements as well as frequencydomain features obtained from acoustic signals. As used herein, the termacoustic signals refers to signals representative of measurements ofacoustic sounds, dynamic strain, vibrations, and the like, whether ornot within the audible or auditory range.

In some embodiments, temperature features can be determined fromtemperature measurements taken along a length being monitored, such as alength of a wellbore. The temperature measurements can be used in afirst or event identification/detection model that can provide an outputindicative of event location(s), for example, fluid inflow locationsalong a wellbore. This can allow those locations with the event (e.g.,fluid inflow) to be identified using temperature based measurements(e.g., from the wellbore). When combined with a (e.g., distributed)temperature sensing system that can provide distributed and continuoustemperature measurements, the systems can allow for event (e.g., fluidinflow) locations to be tracked through time.

The systems described herein also allow for various frequency domainfeatures to be obtained from an acoustic signal originating from theevent (e.g., within the wellbore). The acoustic signals can be obtainedusing a distributed acoustic sensing (DAS) system that allows forcontinuous and distributed acoustic sensing. The acoustic signals can betaken along the same portions of the length (e.g., length of thewellbore) as the temperature measurements, thereby allowing forinformation about the events (e.g., fluid inflow events), to bedetermined using both the temperature features and the frequency domainfeatures. In some embodiments, a second or extent model can be developedand used with one or more frequency domain features that can allow forthe prediction of an extent of the event (e.g., fluid inflow rates forone or more fluids and/or fluid phases of a fluid inflow event).

When used together, the first or event identification model can allowthe event locations to be determined using temperature features, and thesecond or extent model can allow for event (e.g., fluid inflow) extent(e.g., fluid inflow rates) to be determined at the one or more eventlocations. The second or event extent model can be used to verify orvalidate the event locations as determined from the first or eventidentification model. This can help to provide an improved eventlocation determination for use in managing the event.

The second model can also be used to determine the event extents (e.g.,fluid inflow rates of one or more fluids, fluid phases (e.g., combinedgas glow, combined liquid flow, etc.), or both) from one or morelocations (e.g., along the wellbore). The processing can be combinedsuch that the second or event extent model may only be executed at theone or more locations as identified using the temperature features withthe first or event identification model. The resulting event extents(e.g., fluid inflow rates) as determined from the second model can beused to provide an indication of the event extents at the one or morelocations. The resulting event extent (e.g., fluid inflow rate) can alsobe normalized across the one or more locations to provide a relativecontribution to the total event extent (e.g., fluid inflow rates) at theone or more locations. This information can be used along withindependently measured extent values (e.g., actual fluid values (e.g.,production rates)) to provide an improved determination of the actualevent extents (e.g., fluid inflow rates) at one or more locations alongthe length being examined (e.g., one or more locations within thewellbore). This can allow for near real time information on the event tomore effectively manage the event.

Overview

Disclosed herein are systems and methods for determining the presenceand/or extent of events at one or more event locations, for example,within a subterranean wellbore, so that a wellbore operator may moreeffectively control the fluid production from the wellbore. The systemsallow for an identification of the events and/or event extents at theevent location(s) using temperature features derived from temperaturemeasurements and acoustic features derived from acoustic measurements.

The new signal processing architecture disclosed herein allows for theidentification of various events (e.g., the detection of the presence ofthe event at one or more locations). In some embodiments, the events canoccur within a wellbore such as fluid inflow event (e.g., includingfluid inflow detection, fluid inflow location determination, fluidinflow quantification, fluid inflow discrimination, etc.), fluid outflowdetection (e.g., fluid outflow detection, fluid outflow quantification),fluid phase segregation, fluid flow discrimination within a conduit,well integrity monitoring, including in-well leak detection (e.g.,downhole casing and tubing leak detection, leaking fluid phaseidentification, etc.), flow assurance (e.g., wax deposition), annularfluid flow diagnosis, overburden monitoring, fluid flow detection behinda casing, fluid induced hydraulic fracture detection in the overburden(e.g., micro-seismic events, etc.), sand detection (e.g., sand ingress,sand flows, etc.), and the like, each in real time or near real time insome embodiments. Other events can also be detected such as securityevents, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoringevents, dam monitoring events, and the like.

As utilized herein, “fluid flow discrimination” indicates anidentification and/or assignment of the detected fluid flow (e.g.,single phase flow, mixed phase flows, time-based slugging, alteringfluid flows, etc.), gas inflow, hydrocarbon liquid (e.g., coin inflow,and/or aqueous phase (e.g., water) inflow, including any combined ormultiphase flows or inflows. The methods of this disclosure can thus beutilized, in embodiments, to provide information on various events suchas a fluid ingress and/or a fluid ingress point or location as well asflow regimes within a conduit rather than simply a location at whichgas, water, or hydrocarbon liquid is present in the wellbore tubular(e.g., present in a flowing fluid), which can occur at any point abovethe ingress location as the fluid flows to the surface of the wellbore.In some embodiments, the system allows for a quantitative measurement ofvarious fluid flows such as a relative concentration of in-wellhydrocarbon liquid, water, and gas.

In some instances, the systems and methods can provide information inreal time or near real time. As used herein, the term “real time” refersto a time that takes into account various communication and latencydelays within a system, and can include actions taken within about tenseconds, within about thirty seconds, within about a minute, withinabout five minutes, or within about ten minutes of the action occurring.Various sensors (e.g., distributed temperature sensing sensors,distributed fiber optic acoustic sensors, etc.) can be used to obtain adistributed temperature signal and an acoustic signal at various pointsalong a length being monitored, for example, along a wellbore. Thedistributed temperature sensing signal and the acoustic signal can thenbe processed using signal processing architecture with various featureextraction techniques (e.g., temperature feature extraction techniques,spectral feature extraction techniques) to obtain a measure of one ormore temperature features, one or more frequency domain features, and/orcombinations thereof that enable selectively extracting the distributedtemperature sensing signals and acoustic signals of interest frombackground noise and consequently aiding in improving the accuracy ofthe identification of events, including, for example, the movement offluids (e.g., gas inflow locations, water inflow locations, hydrocarbonliquid inflow locations, etc.) in real time. While discussed in terms ofbeing real time in some instances, the data can also be analyzed at alater time at the same location and/or a displaced location.

As used herein, various frequency domain features can be obtained fromthe acoustic signal, and in some contexts, the frequency domain featurescan also be referred to herein as spectral features or spectraldescriptors. In some embodiments, the spectral features can compriseother features, including those in the time domain, various transforms(e.g., wavelets, Fourier transforms, etc.), and/or those derived fromportions of the acoustic signal or other sensor inputs. Such otherfeatures can be used on their own or in combination with one or morefrequency domain features, including in the development oftransformations of the features, as described in more detail herein.

In some embodiments, the distributed temperature sensing signals and theacoustic signal(s) can be obtained in a manner that allows for a signalto be obtained along a length of the sensor, for example, an entirewellbore or a portion of interest (e.g., a depth) thereof, a portion ofa fiber in a security perimeter, the length of a portion thereof of apipeline, or the like. In wellbore contexts, production logging systemsutilize a production logging system (PLS) to determine flow profile inwells. However, since the PLS can be 10-20 meters long and the sensorsare distributed along the length, sensors that are not at the front ofthe PLS are not actually taking measurements at the depth for which themeasurements are recorded, and, thus, the data can be incorrect orincomplete over time. Furthermore, the flow can be altered by the merepresence of the PLS within the wellbore, so what is measured at thedownstream end of the PLS is not an accurate reflection of what theprofile/regime was before the tool disturbed the flow. Furthermore, as aPLS is typically run through a well once or a few times (down and thenup once or a few times and out), and the sensors are exposed to theconditions at a given depth for only a very brief period of time (e.g.,4-5 seconds). Accordingly, while PLSs can provide an indication thatcertain events, such as downhole water inflow, may be occurring, they donot provide continuous measurements over prolonged durations of timethat would be needed to study dynamic variabilities in productionprofiles over time.

Fiber optic distributed temperature sensors (DTS) and fiber opticdistributed acoustic sensors (DAS) can capture distributed temperaturesensing and acoustic signals, respectively, resulting from downholeevents, such as wellbore events (e.g., gas inflow/flow, hydrocarbonliquid inflow/flow, water inflow/flow, mixed flow, leaks, overburdenmovement, and the like), as well as other background events. DTS and DAScan also be used to capture distributed temperature sensing and acousticsignals, respectively, from events, such as security events,transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoringevents, dam monitoring events, and the like. This allows for signalprocessing procedures that distinguish events and flow signals fromother sources to properly identify each type of event. This in turnresults in a need for a clearer understanding of the fingerprint ofin-well event of interest (e.g., fluid inflow, water inflow, gas inflow,hydrocarbon liquid inflow, fluid flow along the tubulars, etc.) in orderto be able to segregate and identify a signal resulting from an event ofinterest from other ambient background signals. As used herein, theresulting fingerprint of a particular event can also be referred to asan event signature, as described in more detail herein. In someembodiments, the temperature features and the acoustic features can eachbe used with a model (e.g., a machine learning model, multivariatemodel, etc.) to provide for detection, identification, and/ordetermination of the extents of various events. A number of differentmodels can be developed and used to determine when and where certainevents have occurred, for example, within a wellbore and/or the extentsof such events.

The ability to identify various events (e.g., wellbore events, securityevents, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoringevents, dam monitoring events, etc.) may allow for various actions orprocesses to be taken in response to the events. For example, reducingdeferrals in wellbores resulting from one or more events such as wateringress and facilitating effective remediation relies upon accurate andtimely decision support to inform the operator of the events. As anotherexample, with respect to events within a wellbore, a well can be shutin, production can be increased or decreased, and/or remedial measurescan be taken in the wellbore, as appropriate based on the identifiedevent(s). As another example, the detection of a pipeline leak can allowthe operator to develop a plan to fix the leak with minimal downtime inthe pipeline. An effective response, when needed, benefits not just froma binary yes/no output of an identification/detection of in-well eventsbut also from a measure of an extent, such as a relative amount offluids (e.g., amount of gas inflow, amount of hydrocarbon liquid inflow,amount of water inflow, etc.) from each of the identified zones ofevents so that zones contributing the greatest fluid amount(s) can beacted upon first to improve or optimize production. The systems andmethods described herein can be used, in applications, to identify thesource of an event or problem, as well as additional information aboutthe event (referred to herein as an “extent” of the event), such as adirection and amount of flow, and/or an identification of the type ofproblem being faced. For example, when an event comprising water inflowand a location thereof are detected, determination of an extent of theinflow event comprising a relative flow rate of the hydrocarbon liquidat the water inflow location may allow for a determination of whether ornot to remediate, the type or method of remediation, the timing forremediation, and/or deciding to alter (e.g., reduce) a production ratefrom the well. For example, production zones can be isolated, productionassemblies can be open, closed, or choked at various levels, side wellscan be drilled or isolated, and the like. Such determinations can beused to improve on the drawdown of the well while reducing theproduction expenses associated with various factors such as producedwater.

The same signal processing described herein can be used to identifyvarious events across a variety of industries. The systems can comprisesimilar real time signal processing architecture that allows for theidentification of events using various signatures or models. Withinthese systems, various sensors (e.g., distributed temperature sensors,point temperature sensors, distributed fiber optic acoustic sensors,point acoustic sensors, etc.) can be used to obtain a sampling atvarious points along a path or length. The distributed temperaturesensing signal and acoustic signals can then be processed using signalprocessing architecture with temperature feature and spectral featureextraction techniques, as detailed hereinbelow, to obtain temperaturefeatures and acoustic features, respectively, that enable selectivelyextracting the signals of interest from background noise.

Once obtained, the temperature and acoustic features can be used invarious models in order to be able to segregate a noise resulting froman event of interest from other ambient background noise. Specificmodels can be determined for each event by considering one or moretemperature features and/or acoustic features for known events. Fromthese known events, the temperature and/or acoustic features specific toeach event can be developed and signatures (e.g., having ranges orthresholds) and/or models can be established to determine a presence (orabsence) of each event. Based on the specifics of each temperatureand/or acoustic feature, the resulting signatures or models can be usedto sufficiently distinguish between events to allow for a relativelyfast identification of such events. The resulting signatures or modelscan then be used along with processed distributed temperature sensingand/or acoustic signal data to determine if an event is occurring at apoint of interest along the path of the temperature and/or acousticsensor(s). Any of the processing techniques disclosed herein can be usedto initially determine a signature or model(s), and then process andcompare the temperature and/or acoustic features in a sampledtemperature sensing and/or acoustic signal with the resulting signaturesor model(s).

Thus, temperature and acoustic signals in industries such as security(e.g., security, pipeline monitoring, etc.), energy (e.g., geothermal,etc.), transportation (e.g., railway monitoring, roadway monitoring,etc.), and facilities monitoring (e.g., monitoring equipment such aselectric submersible pumps, wind turbines, compressors, dams, etc.) canbenefit from the use of the systems and methods disclosed herein. Forexample, a pipeline can be monitored to detect temperature and/oracoustic signals along the length of a pipeline, using for example, afiber attached to the pipeline, along with a DTS and/or DAS unit. Thelength of the fiber along the pipeline can be considered a path of thefiber as it passes from the receiver/generator (e.g., the DTS and/or DASunit) along the pipeline. Various temperature and/or acoustic signaturescan be detected based on temperature sensing and/or acoustic signalsoriginating along the length of the pipeline and/or fiber. These signalscan be processed to extract one or more temperature and/or spectralfeatures, and signatures and/or model(s) of such events can bedetermined or developed. Once obtained, the signatures and/or model(s)can be used to process distributed temperature sensing and/or acousticsignals at various lengths along the path of the fiber and determine thepresence or absence of the various events using the temperature and/orspectral features and the signatures and/or model(s).

Similarly, the temperature and/or acoustic monitoring techniquesdescribed herein can be used with one or more point sources, which canbe individual or connected along a path. For example, a facility havingindustrial equipment can be monitored using the temperature and/oracoustic monitoring techniques described herein. For example, a facilityhaving pumps, turbines, compressors, or other equipment can have atemperature and/or acoustic sensor(s) monitoring the piece of equipment.Signatures and/or model(s) of various events can be determined for eachtype of equipment and used to monitor and identify the state of theequipment. For example, a pump can be monitored to determine if the pumpis active or inactive through the use of a temperature and/or acousticsignal and the temperature and/or spectral characteristics and/ormodel(s) determining the presence of an event as described herein. Whenmultiple piece of equipment are present, a single distributedtemperature sensor and/or acoustic sensor, such as a fiber, can becoupled to each piece of equipment. This configuration may allow asingle interrogation unit to monitor multiple pieces of equipment usingthe analysis by resolving a length along the fiber for each piece ofequipment. Thus, a distributed temperature and/or acoustic monitoringsystem may not require multiple processors correlating to individualpieces of equipment.

Similarly, pipelines can be monitored in a manner similar to the way thewellbores are monitored as disclosed herein. In this embodiment, thefiber may detect various events such as leaks, flow over a blockage orcorrosion, and the like. This may allow for remote monitoring along thelength of a pipeline.

Other types of industries can also benefit from the use of temperatureand/or acoustic sensing to obtain temperature and/or acoustic signalsthat can be analyzed and matched to events using temperature andspectral feature extraction, respectively, as described hereinbelow. Anyindustry that experiences events that create temperature and/or acousticsignals can be monitored using the systems and methods as describedherein. Further, when the signals are distributed across space, a singletemperature and/or acoustic sensor, such as an optical fiber, can beused with a receiver unit to detect temperature and/or acoustic signalsacross the length or path of the sensor element, thereby enabling asingle sensor to detect temperature sensing and/or acoustic signalsacross a wide area or path. In some embodiments, a point sensor such asa temperature thermocouple can be used to obtain a temperature from alocation and used with the processes described herein to detect anevent. In these embodiments, the signal may not be obtained from awellbore. For example, the temperature and/or acoustic signals may beobtained from a non-wellbore source or from outside of a subterraneanformation. Thus, the systems and processing techniques described hereincan be used to identify events using temperature and/or acousticfeatures obtained from temperature sensing and/or acoustic signalsacross a variety of industries and locations.

Herein described are systems and methods for detecting (e.g.,identifying) and characterizing events (e.g., wellbore events, securityevents, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoringevents, dam monitoring events, etc.). In some embodiments, the wellboreevents can comprise fluid inflow locations and/or fluid flow regimeswithin a conduit in the wellbore. In some embodiments, other wellboreevents such as fluid outflow detection, fluid phase segregation, fluidflow discrimination within a conduit, well integrity monitoring, in-wellleak detection, annular fluid flow diagnosis, overburden monitoring,fluid flow detection behind a casing, sand detection (e.g., sandingress, sand flows, etc.), and the like can be detected. In someembodiments, events such as security events, transportation events,geothermal events, carbon capture and CO₂ injection events, facilitymonitoring events, pipeline monitoring events, dam monitoring events,and the like can be detected. For example, temperature and/or acousticsignals can be used in security monitoring to detect open doorways, gapsor holes in a perimeter, or the like. Similarly, temperature and/oracoustic signals can be detected on railways to determine the presenceof trains through friction based heating, and ice can be detected onrailways or roadways through the use of the temperature and/or acousticsignals. Geothermal events, carbon capture events, and CO₂ injectionevents can be similar to wellbore events with respect to geothermalfluid inflow, outflows, leaks, and the like. Pipelines can also bemonitored for flow, leaks, blockages, and the like.

As described herein, temperature features and/or spectral descriptors orfrequency domain features can be used with DTS temperature and/or DASacoustic data processing, respectively, to provide for event detectionand/or event extent determination. For example, the temperature and/orspectral features can be used with wellbore eventdetection/identification (e.g., fluid profiling, fluid inflow locationdetection, fluid phase discrimination such as the determination that thefluid at one or more locations such as the detected fluid inflowlocation comprises gas inflow, hydrocarbon liquid inflow, aqueous phaseinflow, a combined fluid flow, and/or a time varying fluid flow such asslugging single or multiphase flow, and the like). In some embodiments,a first or event identification/detection model can be used for eventidentification. The first or event identification model can comprise oneor more individual models, which can be the same or different asdescribed in more detail herein. In some embodiments, the first or eventidentification model can comprise a plurality of sub-models such as afluid flow model used for inflow fluid phase discrimination, which canallow for the determination of at least one of a gas phase inflow, anaqueous phase inflow, a hydrocarbon liquid phase inflow, and variouscombinational flow regimes in a wellbore. In some embodiments, the sameor a different event identification models can be used to identify otherevents such as fluid flow phase discrimination to determine thecomposition of fluid flowing in a conduit. Suitable event detectionmodel(s) can be developed for any of the events described herein.

Application of the signal processing techniques and one or more eventdetection models with DTS and/or DAS for wellbore events such asdownhole surveillance can provide a number of benefits includingimproving reservoir recovery by monitoring efficient drainage ofreserves through downhole fluid surveillance (e.g., production flowmonitoring), improving well operating envelopes through identificationof drawdown levels (e.g., gas, water, etc.), facilitating targetedremedial action for efficient well management and well integrity,reducing operational risk through the clear identification of anomaliesand/or failures in well barrier elements. Similar advantages are alsopossible with other non-wellbore events.

In some embodiments, use of the systems and methods described herein mayprovide knowledge of the events and the locations experiencing variousevents, thereby potentially allowing for improved actions (e.g.,remediation actions for wellbore events, security actions for securityevents, etc.) based on the processing results. The methods and systemsdisclosed herein can also provide information on the events. Forexample, for wellbore events, information about a variability of theamount of fluid inflow being produced by the different fluid influxzones as a function of different production rates, different productionchokes, and downhole pressure conditions can be determined, therebyenabling control of fluid inflow. For fluid inflow events, embodimentsof the systems and methods disclosed herein also allow for a computationof the relative concentrations of fluid ingress (e.g., relative amountsof gas, hydrocarbon liquid, and water in the inflow fluid) into thewellbore, thereby offering the potential for more targeted and effectiveremediation.

As disclosed herein, embodiments of the data processing techniques canuse various sequences of real time digital signal processing steps toidentify the temperature and/or acoustic signal resulting from variousevents from background noise, and allow real time detection of theevents and their locations using distributed fiber optic temperatureand/or acoustic sensor data as the input data feed.

One or more models can be developed using test data to provide a labeleddata set used as input into the model. The resulting trained models canthen be used to identify one or more signatures based on features of thetest data and one or more machine learning techniques to developcorrelations for the presence of various events. In the modeldevelopment, specific events can be created in a test set-up, and thetemperature and/or acoustic signals can be obtained and recorded todevelop test data. The test data can be used to train one or more modelsdefining the various events. The resulting model can then be used todetermine one or more events. In some embodiments, actual field data canbe used and correlated to actual events using inputs from, for example,other temperature sensors, other acoustic sensors, and/or otherproduction sensors (e.g., pressure sensors, flow meters, opticalsensors, etc.). The data can be labeled to create a training data setbased on actual production situations. The data can then be used aloneor in combination with the test data to develop the model(s).

As described herein, wellbore events are used as an example. However, asnoted above, other events and event detection model(s) for the otherevents are also within the scope of this disclosure. As describedherein, the systems and methods can be used to identify the presenceand/or extent of one or more events. Various events can be determinedusing the system and method, such wellbore events including, withoutlimitation, fluid outflow detection, fluid phase segregation, fluid flowdiscrimination within a conduit, well integrity monitoring, in well leakdetection, annular fluid flow diagnosis, overburden monitoring, fluidflow detection behind a casing, wax deposition events, sand detection(e.g., sand ingress, sand flows, etc.), security events, transportationevents, geothermal events, carbon capture and CO₂ injection events,facility monitoring events, pipeline monitoring events, dam monitoringevents, and the like. Fluid flow can comprise fluid flow along or withina tubular within a wellbore such as fluid flow within a productiontubular. Fluid flow can also comprise fluid flow from the reservoir orformation into a wellbore tubular. Such flow into the wellbore and/or awellbore tubular can be referred to as fluid inflow. While fluid inflowmay be separately identified at times in this disclosure, such fluidinflow is considered a part of fluid flow within the wellbore.

In some embodiments, temperature features and/or acoustic features canbe determined from respective measurements taken along a length, forexample, a length of a wellbore. In some embodiments, the temperatureand/or acoustic measurements can be used with one or more temperatureand/or acoustic signatures, respectively, to determine the presence ofabsence of an event. The signatures can comprise a number of thresholdsor ranges for comparison with various temperature features. When thedetected temperature features fall within the signatures, the event maybe determined to be present. In some embodiments, the temperaturemeasurements can be used in a first or event detection model that canprovide an output indicative of the presence or absence of one or moreevents (and optionally also one or more event locations) along thelength (e.g., along the length of the wellbore). This can allow eventsto be identified using temperature based measurements (e.g., from thewellbore). When combined with a distributed temperature sensing systemthat can provide distributed and continuous temperature measurements,the systems can allow for fluid inflow locations to be tracked throughtime. A second or event extent model can utilize one or more spectralfeatures to determine an extent of the event at the one or morelocations.

A DAS/DTS system of this disclosure will now be described with referenceto a wellbore. As noted above, a hybrid DAS/DTS system of thisdisclosure can be applied in non-wellbore applications, and thefollowing wellbore description should not be limiting.

Referring now to FIG. 1 , a schematic, cross-sectional illustration of adownhole wellbore operating environment 101 according to someembodiments is shown. More specifically, environment 101 includes awellbore 114 traversing a subterranean formation 102, casing 112 liningat least a portion of wellbore 114, and a tubular 120 extending throughwellbore 114 and casing 112. A plurality of completion assemblies suchas spaced screen elements or assemblies 118 may be provided alongtubular 120 at one or more production zones 104 a, 104 b within thesubterranean formation 102. In particular, two production zones 104 a,104 b are depicted within subterranean formation 102 of FIG. 1 ;however, the precise number and spacing of the production zones 104 a,104 b may be varied in different embodiments. The completion assembliescan comprise flow control devices such as sliding sleeves, adjustablechokes, and/or inflow control devices to allow for control of the flowfrom each production zone. The production zones 104 a, 104 b may belayers, zones, or strata of formation 102 that contain hydrocarbonfluids (e.g., oil, gas, condensate, etc.) therein.

In addition, a plurality of spaced zonal isolation devices 117 andgravel packs 122 may be provided between tubular 120 and the sidewall ofwellbore 114 at or along the interface of the wellbore 114 with theproduction zones 104 a, 104 b. In some embodiments, the operatingenvironment 101 includes a workover and/or drilling rig positioned atthe surface and extending over the wellbore 114. While FIG. 1 shows anexample completion configuration in FIG. 1 , it should be appreciatedthat other configurations and equipment may be present in place of or inaddition to the illustrated configurations and equipment. For example,sections of the wellbore 114 can be completed as open hole completionsor with gravel packs without completion assemblies.

In general, the wellbore 114 can be formed in the subterranean formation102 using any suitable technique (e.g., drilling). The wellbore 114 canextend substantially vertically from the earth's surface over a verticalwellbore portion, deviate from vertical relative to the earth's surfaceover a deviated wellbore portion, and/or transition to a horizontalwellbore portion. In general, all or portions of a wellbore may bevertical, deviated at any suitable angle, horizontal, and/or curved. Inaddition, the wellbore 114 can be a new wellbore, an existing wellbore,a straight wellbore, an extended reach wellbore, a sidetracked wellbore,a multi-lateral wellbore, and other types of wellbores for drilling andcompleting one or more production zones. As illustrated, the wellbore114 includes a substantially vertical producing section 150 whichincludes the production zones 104 a, 104 b. In this embodiment,producing section 150 is an open-hole completion (i.e., casing 112 doesnot extend through producing section 150). Although section 150 isillustrated as a vertical and open-hole portion of wellbore 114 in FIG.1 , embodiments disclosed herein can be employed in sections ofwellbores having any orientation, and in open or cased sections ofwellbores. The casing 112 extends into the wellbore 114 from the surfaceand can be secured within the wellbore 114 with cement 111.

The tubular 120 may comprise any suitable downhole tubular or tubularstring (e.g., drill string, casing, liner, jointed tubing, and/or coiledtubing, etc.), and may be inserted within wellbore 114 for any suitableoperation(s) (e.g., drilling, completion, intervention, workover,treatment, production, etc.). In the embodiment shown in FIG. 2 , thetubular 120 is a completion assembly string. In addition, the tubular120 may be disposed within in any or all portions of the wellbore 114(e.g., vertical, deviated, horizontal, and/or curved section of wellbore114).

In this embodiment, the tubular 120 extends from the surface to theproduction zones 104 a, 104 b and generally provides a conduit forfluids to travel from the formation 102 (particularly from productionzones 104 a, 104 b) to the surface. A completion assembly including thetubular 120 can include a variety of other equipment or downhole toolsto facilitate the production of the formation fluids from the productionzones. For example, zonal isolation devices 117 can be used to isolatethe production zones 104 a, 104 b within the wellbore 114. In thisembodiment, each zonal isolation device 117 comprises a packer (e.g.,production packer, gravel pack packer, frac-pac packer, etc.). The zonalisolation devices 117 can be positioned between the screen assemblies118, for example, to isolate different gravel pack zones or intervalsalong the wellbore 114 from each other. In general, the space betweeneach pair of adjacent zonal isolation devices 117 defines a productioninterval, and each production interval may correspond with one of theproduction zones 104 a, 104 b of subterranean formation 102.

The screen assemblies 118 provide sand control capability. Inparticular, the sand control screen elements 118, or other filter mediaassociated with wellbore tubular 120, can be designed to allow fluids toflow therethrough but restrict and/or prevent particulate matter ofsufficient size from flowing therethrough. The screen assemblies 118 canbe of any suitable type such as the type known as “wire-wrapped”, whichare made up of a wire closely wrapped helically about a wellboretubular, with a spacing between the wire wraps being chosen to allowfluid flow through the filter media while keeping particulates that aregreater than a selected size from passing between the wire wraps. Othertypes of filter media can also be provided along the tubular 120 and caninclude any type of structures commonly used in gravel pack wellcompletions, which permit the flow of fluids through the filter orscreen while restricting and/or blocking the flow of particulates (e.g.other commercially-available screens, slotted or perforated liners orpipes; sintered-metal screens; sintered-sized, mesh screens; screenedpipes; prepacked screens and/or liners; or combinations thereof). Aprotective outer shroud having a plurality of perforations therethroughmay be positioned around the exterior of any such filter medium.

The gravel packs 122 can be formed in the annulus 119 between the screenelements 118 (or tubular 120) and the sidewall of the wellbore 114 in anopen hole completion. In general, the gravel packs 122 compriserelatively coarse granular material placed in the annulus to form arough screen against the ingress of sand into the wellbore while alsosupporting the wellbore wall. The gravel pack 122 is optional and maynot be present in all completions.

In some embodiments, one or more of the completion assemblies cancomprise flow control elements such as sliding sleeves, chokes, valves,or other types of flow control devices that can control the flow of afluid from an individual production zone or a group of production zones.The force on the production face can then vary based on the type ofcompletion within the wellbore and/or each production zone (e.g., in asliding sleeve completion, open hole completion, gravel pack completion,etc.). In some embodiments, a sliding sleeve or other flow controlledproduction zone can experience a force on the production face that isrelatively uniform within the production zone, and the force on theproduction face can be different between each production zone. Forexample, a first production zone can have a specific flow controlsetting that allows the production rate from the first zone to bedifferent than the production rate from a second production zone. Thus,the choice of completion type (e.g., which can be specified in acompletion plan) can affect on the need for or the ability to provide adifferent production rate within different production zones.

Referring still to FIG. 1 , a monitoring system 110 can comprise anacoustic monitoring system and/or a temperature monitoring system. Themonitoring system 1110 can be positioned in the wellbore 114. Asdescribed herein, the monitoring system 110 may be utilized to detect ormonitor fluid inflow event(s) into the wellbore 114. The variousmonitoring systems (e.g., acoustic monitoring systems, temperaturemonitoring systems, etc.) may be referred to herein as an “inflowdetection system,” and/or an “inflow monitoring system.”

The monitoring system 110 comprises an optical fiber 162 that is coupledto and extends along tubular 120. In cased completions, the opticalfiber 162 can be installed between the casing and the wellbore wallwithin a cement layer and/or installed within the casing or productiontubing. Referring briefly to FIGS. 2A and 2B, optical fiber 162 of themonitoring system 110 may be coupled to an exterior of tubular 120(e.g., such as shown in FIG. 2B) or an interior of tubular (e.g., suchas shown in FIG. 2A). When the optical fiber 162 is coupled to theexterior of the tubular 120, as depicted in the embodiment of FIG. 2B,the optical fiber 162 can be positioned within a control line, controlchannel, or recess in the tubular 120. In some embodiments an outershroud contains the tubular 120 and protects the optical fiber 162during installation. A control line or channel can be formed in theshroud and the optical fiber 162 can be placed in the control line orchannel (not specifically shown in FIGS. 2A and 2B).

Referring again to FIG. 1 , generally speaking, during operation of athe monitoring system, an optical backscatter component of lightinjected into the optical fiber 162 may be used to detect variousconditions incident on the optical fiber such as acoustic perturbations(e.g., dynamic strain), temperature, static strain, and the like alongthe length of the optical fiber 162. The light can be generated by alight generator or source 166 such as a laser, which can generate lightpulses. The light used in the system is not limited to the visiblespectrum, and light of any frequency can be used with the systemsdescribed herein. Accordingly, the optical fiber 162 acts as the sensorelement with no additional transducers in the optical path, andmeasurements can be taken along the length of the entire optical fiber162. The measurements can then be detected by an optical receiver suchas sensor 164 and selectively filtered to obtain measurements from agiven depth point or range, thereby providing for a distributedmeasurement that has selective data for a plurality of zones (e.g.,production zones 104 a, 104 b) along the optical fiber 162 at any giventime. For example, time of flight measurements of the backscatteredlight can be used to identify individual zones or measurement lengths ofthe fiber optic 162. In this manner, the optical fiber 162 effectivelyfunctions as a distributed array of sensors spread over the entirelength of the optical fiber 162, which typically across production zones104 a, 104 b within the wellbore 114.

The light backscattered up the optical fiber 162 as a result of theoptical backscatter can travel back to the source, where the signal canbe collected by a sensor 164 and processed (e.g., using a processor168). In general, the time the light takes to return to the collectionpoint is proportional to the distance traveled along the optical fiber162, thereby allowing time of flight measurements of distance along theoptical fiber. The resulting backscattered light arising along thelength of the optical fiber 162 can be used to characterize theenvironment around the optical fiber 162. The use of a controlled lightsource 166 (e.g., having a controlled spectral width and frequency) mayallow the backscatter to be collected and any parameters and/ordisturbances along the length of the optical fiber 162 to be analyzed.In general, the various parameters and/or disturbances along the lengthof the optical fiber 162 can result in a change in the properties of thebackscattered light.

An acquisition device 160 may be coupled to one end of the optical fiber162 that comprises the sensor 164, light generator 166, a processor 168,and a memory 170. As discussed herein, the light source 166 can generatethe light (e.g., one or more light pulses), and the sensor 164 cancollect and analyze the backscattered light returning up the opticalfiber 162. In some contexts, the acquisition device 160 (which comprisesthe light source 166 and the sensor 164 as noted above), can be referredto as an interrogator. The processor 168 may be in signal communicationwith the sensor 164 and may perform various analysis steps described inmore detail herein. While shown as being within the acquisition device160, the processor 168 can also be located outside of the acquisitiondevice 160 including being located remotely from the acquisition device160. The sensor 164 can be used to obtain data at various rates and mayobtain data at a sufficient rate to detect the acoustic signals ofinterest with sufficient bandwidth. While described as a sensor 164 in asingular sense, the sensor 164 can comprise one or more photodetectorsor other sensors that can allow one or more light beams and/orbackscattered light to be detected for further processing. In anembodiment, depth resolution ranges in a range of from about 1 meter toabout 10 meters, or less than or equal to about 10, 9, 8, 7, 6, 5, 4, 3,2, or 1 meter can be achieved. Depending on the resolution needed,larger averages or ranges can be used for computing purposes. When ahigh depth resolution is not needed, a system may have a widerresolution (e.g., which may be less expensive) can also be used in someembodiments. Data acquired by the monitoring system 110 (e.g., via fiber162, sensor 164, etc.) may be stored on memory 170.

The monitoring system 110 can be used for detecting a variety ofparameters and/or disturbances in the wellbore including being used todetect temperatures along the wellbore, acoustic signals along thewellbore, static strain and/or pressure along the wellbore, or anycombination thereof.

In some embodiments, the monitoring system 110 can be used to detecttemperatures within the wellbore. The temperature monitoring system caninclude a distributed temperature sensing (DTS) system. A DTS system canrely on light injected into the optical fiber 162 along with thereflected signals to determine a temperature and/or strain based onoptical time-domain reflectometry. In order to obtain DTS measurements,a pulsed laser from the light generator 166 can be coupled to theoptical fiber 162 that serves as the sensing element. The injected lightcan be backscattered as the pulse propagates through the optical fiber162 owing to density and composition as well as to molecular and bulkvibrations. A portion of the backscattered light can be guided back tothe acquisition device 160 and split of by a directional coupler to asensor 164. It is expected that the intensity of the backscattered lightdecays exponentially with time. As the speed of light within the opticalfiber 162 is known, the distance that the light has passed through theoptical fiber 162 can be derived using time of flight measurements.

In both distributed acoustic sensing (DAS) and DTS systems, thebackscattered light includes different spectral components which containpeaks that are known as Rayleigh and Brillouin peaks and Raman bands.The Rayleigh peaks are independent of temperature and can be used todetermine the DAS components of the backscattered light. The Ramanspectral bands are caused by thermally influenced molecular vibrations.The Raman spectral bands can then be used to obtain information aboutdistribution of temperature along the length of the optical fiber 162disposed in the wellbore.

The Raman backscattered light has two components, Stokes andAnti-Stokes, one being only weakly dependent on temperature and theother being greatly influenced by temperature. The relative intensitiesbetween the Stokes and Anti-Stokes components and are a function oftemperature at which the backscattering occurred. Therefore, temperaturecan be determined at any point along the length of the optical fiber 162by comparing at each point the Stokes and Anti-stokes components of thelight backscattered from the particular point. The Brillouin peaks maybe used to monitor strain along the length of the optical fiber 162.

The DTS system can then be used to provide a temperature measurementalong the length of the wellbore during the production of fluids,including fluid inflow events. The DTS system can represent a separatesystem from the DAS system or a single common system, which can compriseone or more acquisition devices in some embodiments. In someembodiments, a plurality of fibers 162 are present within the wellbore,and the DAS system can be coupled to a first optical fiber and the DTSsystem can be coupled to a second, different, optical fiber.Alternatively, a single optical fiber can be used with both systems, anda time division multiplexing or other process can be used to measureboth DAS and DTS on the same optical fiber.

In an embodiment, depth resolution for the DTS system can range fromabout 1 meter to about 10 meters, or less than or equal to about 10, 9,8, 7, 6, 5, 4, 3, 2, or 1 meter can be achieved. Depending on theresolution needed, larger averages or ranges can be used for computingpurposes. When a high depth resolution is not needed, a system may havea wider resolution (e.g., which may be less expensive) can also be usedin some embodiments. Data acquired by the DTS system 110 (e.g., viafiber 162, sensor 164, etc.) may be stored on memory 170.

While the temperature monitoring system described herein can use a DTSsystem to acquire the temperature measurements for a location or depthrange in the wellbore 114, in general, any suitable temperaturemonitoring system can be used. For example, various point sensors,thermocouples, resistive temperature sensors, or other sensors can beused to provide temperature measurements at a given location based onthe temperature measurement processing described herein. Further, anoptical fiber comprising a plurality of point sensors such as Bragggratings can also be used. As described herein, a benefit of the use ofthe DTS system is that temperature measurements can be obtained across aplurality of locations and/or across a continuous length of the wellbore114 rather than at discrete locations.

The monitoring system 110 can comprise an acoustic monitoring system tomonitor acoustic signals within the wellbore. The acoustic monitoringsystem can comprise a DAS based system, though other types of acousticmonitoring systems, including other distributed monitoring systems, canalso be used.

During operation of a DAS system an optical backscatter component oflight injected into the optical fiber 162 (e.g., Rayleigh backscatter)may be used to detect acoustic perturbations (e.g., dynamic strain)along the length of the fiber 162. The light backscattered up theoptical fiber 162 as a result of the optical backscatter can travel backto the source, where the signal can be collected by a sensor 164 andprocessed (e.g., using a processor 168) as described herein. In general,any acoustic or dynamic strain disturbances along the length of theoptical fiber 162 can result in a change in the properties of thebackscattered light, allowing for a distributed measurement of both theacoustic magnitude (e.g., amplitude), frequency and, in some cases, ofthe relative phase of the disturbance. Any suitable detection methodsincluding the use of highly coherent light beams, compensatinginterferometers, local oscillators, and the like can be used to produceone or more signals that can be processed to determine the acousticsignals or strain impacting the optical fiber along its length.

While the system 101 described herein can be used with a DAS system(e.g., DAS system 110) to acquire an acoustic signal for a location ordepth range in the wellbore 114, in general, any suitable acousticsignal acquisition system can be used in performing embodiments ofmethod 10 (see e.g., FIG. 1 ). For example, various microphones,geophones, hydrophones, or other sensors can be used to provide anacoustic signal at a given location based on the acoustic signalprocessing described herein. Further, an optical fiber comprising aplurality of point sensors such as Bragg gratings can also be used. Asdescribed herein, a benefit of the use of the DAS system 110 is that anacoustic signal can be obtained across a plurality of locations and/oracross a continuous length of the wellbore 114 rather than at discretelocations.

The monitoring system 110 can be used to generate temperaturemeasurements and/or acoustic measurements along the length of thewellbore. The resulting measurements can be processed to obtain varioustemperature and/or acoustic based features that can then be used toidentify inflow locations, identify inflowing fluid phases, and/orquantify the rate of fluid inflow. Each of the specific types offeatures obtained from the monitoring system are described in moredetail below.

Fluid can be produced into the wellbore 114 and into the completionassembly string. During operations, the fluid flowing into the wellboremay comprise hydrocarbon fluids, such as, for instance hydrocarbonliquids (e.g., oil), gases (e.g., natural gas such as methane, ethane,etc.), and/or water, any of which can also comprise particulates such assand. However, the fluid flowing into the tubular may also compriseother components, such as, for instance steam, carbon dioxide, and/orvarious multiphase mixed flows. The fluid flow can further be timevarying such as including slugging, bubbling, or time altering flowrates of different phases. The amounts or flow rates of these componentscan vary over time based on conditions within the formation 102 and thewellbore 114. Likewise, the composition of the fluid flowing into thetubular 120 sections throughout the length of the entire productionstring (e.g., including the amount of sand contained within the fluidflow) can vary significantly from section to section at any given time.

As the fluid enters the wellbore 114, the fluid can create acousticsignals and temperature changes that can be detected by the monitoringsystem such as the DTS system and/or the DAS systems as describedherein. With respect to the temperature variations, the temperaturechanges can result from various fluid effects within the wellbore suchas cooling based on gas entering the wellbore, temperature changesresulting from liquids entering the wellbore, and various flow relatedtemperature changes as a result of the fluids passing through thewellbore. For example, as fluids enter the wellbore, the fluids canexperience a sudden pressure drop, which can result in a change in thetemperature. The magnitude of the temperature change depends on thephase and composition of the inflowing fluid, the pressure drop, and thepressure and temperature conditions. The other major thermodynamicprocess that takes place as the fluid enters the well is thermal mixingwhich results from the heat exchange between the fluid body that flowsinto the wellbore and the fluid that is already flowing in the wellbore.As a result, inflow of fluids from the reservoir into the wellbore cancause a deviation in the flowing well temperature profile.

By obtaining the temperature in the wellbore, a number of temperaturefeatures can be obtained from the temperature measurements. Thetemperature features can provide an indication of one or moretemperature trends at a given location in the wellbore during ameasurement period. The resulting features can form a distribution oftemperature results that can then be used with various models toidentify one or more events within the wellbore at the location.

The temperature measurements can represent output values from the DTSsystem, which can be used with or without various types ofpre-processing such as noise reduction, smoothing, and the like. Whenbackground temperature measurements are used, the background measurementcan represent a temperature measurement at a location within thewellbore taken in the absence of the flow of a fluid. For example, atemperature profile along the wellbore can be taken when the well isinitially formed and/or the wellbore can be shut in and allowed toequilibrate to some degree before measuring the temperatures at variouspoints in the wellbore. The resulting background temperaturemeasurements or temperature profile can then be used in determining thetemperature features in some embodiments.

In general, the temperature features represent statistical variations ofthe temperature measurements through time and/or depth. For example, thetemperature features can represent statistical measurements or functionsof the temperature within the wellbore that can be used with variousmodels to determine whether or not fluid inflow events have occurred.The temperature features can be determined using various functions andtransformations, and in some embodiments can represent a distribution ofresults. In some embodiments, the temperature features can represent anormal or Gaussian distribution. In some embodiments, the temperaturemeasurements can represent measurement through time and depth, such asvariations taken first with respect to time and then with respect todepth or first with respect to depth and then with respect to time. Theresulting distributions can then be used with models such asmultivariate models to determine the presence of the fluid inflowevents.

In some embodiments, the temperature features can include variousfeatures including, but not limited to, a depth derivative oftemperature with respect to depth, a temperature excursion measurement,a baseline temperature excursion, a peak-to-peak value, a Fast Fouriertransform (FFT), a Laplace transform, a wavelet transform, a derivativeof temperature with respect to depth, a heat loss parameter, anautocorrelation, and combinations thereof.

In some embodiments, the temperature features can comprise a depthderivative of temperature with respect to depth. This feature can bedetermined by taking the temperature measurements along the wellbore andsmoothing the measurements.

Smoothing can comprise a variety of steps including filtering theresults, de-noising the results, or the like. In some embodiments, thetemperature measurements can be median filtered within a given window tosmooth the measurements. Once smoothed, the change in the temperaturewith depth can be determined. In some embodiments, this can includetaking a derivative of the temperature measurements with respect todepth along the longitudinal axis of the wellbore 114. The depthderivative of temperature values can then be processed, and themeasurement with a zero value (e.g., representing a point of no changein temperature with depth) that have preceding and proceeding valuesthat are non-zero and have opposite signs in depth (e.g., zero belowwhich the value is negative and above positive or vice versa) can havethe values assign to the nearest value. This can then result in a set ofmeasurements representing the depth derivative of temperature withrespect to depth.

In some embodiments, the temperature features can comprise a temperatureexcursion measurement. The temperature excursion measurement cancomprise a difference between a temperature reading at a first depth anda smoothed temperature reading over a depth range, where the first depthis within the depth range. In some embodiments, the temperatureexcursion measurement can represent a difference between de-trendedtemperature measurements over an interval and the actual temperaturemeasurements within the interval. For example, a depth range can beselected within the wellbore 114. The temperature readings within a timewindow can be obtained within the depth range and de-trended orsmoothed. In some embodiments, the de-trending or smoothing can includeany of those processes described above, such as using median filteringof the data within a window within the depth range. For medianfiltering, the larger the window of values used, the greater thesmoothing effect can be on the measurements. For the temperatureexcursion measurement, a range of windows from about 10 to about 100values, or between about 20-60 values (e.g., measurements of temperaturewithin the depth range) can be used to median filter the temperaturemeasurements. A difference can then be taken between the temperaturemeasurement at a location and the de-trended (e.g., median filtered)temperature values. The temperature measurements at a location can bewithin the depth range and the values being used for the medianfiltering. This temperature feature then represents a temperatureexcursion at a location along the wellbore 114 from a smoothedtemperature measurement over a larger range of depths around thelocation in the wellbore 114.

In some embodiments, the temperature features can comprise a baselinetemperature excursion. The baseline temperature excursion represents adifference between a de-trended baseline temperature profile and thecurrent temperature at a given depth. In some embodiments, the baselinetemperature excursion can rely on a baseline temperature profile thatcan contain or define the baseline temperatures along the length of thewellbore 114. As described herein, the baseline temperatures representthe temperature as measured when the wellbore 114 is shut in. This canrepresent a temperature profile of the formation in the absence of fluidflow. While the wellbore 114 may affect the baseline temperaturereadings, the baseline temperature profile can approximate a formationtemperature profile. The baseline temperature profile can be determinedwhen the wellbore 114 is shut in and/or during formation of the wellbore114, and the resulting baseline temperature profile can be used overtime. If the condition of the wellbore 114 changes over time, thewellbore 114 can be shut in and a new baseline temperature profile canbe measured or determined. It is not expected that the baselinetemperature profile is re-determined at specific intervals, and ratherit would be determined at discrete times in the life of the wellbore114. In some embodiments, the baseline temperature profile can bere-determined and used to determine one or more temperature featuressuch as the baseline temperature excursion.

Once the baseline temperature profile is obtained, the baselinetemperature measurements at a location in the wellbore 114 can besubtracted from the temperature measurement detected by the temperaturemonitoring system 110 at that location to provide baseline subtractedvalues. The results can then be obtained and smoothed or de-trended. Forexample, the resulting baseline subtracted values can be median filteredwithin a window to smooth the data. In some embodiments, a windowbetween 10 and 500 temperature values, between 50 and 400 temperaturevalues, or between 100 and 300 temperature values can be used to medianfilter the resulting baseline subtracted values. The resulting smoothedbaseline subtracted values can then be processed to determine a changein the smoothed baseline subtracted values with depth. In someembodiments, this can include taking a derivative of the smoothedbaseline subtracted values with respect to depth along the longitudinalaxis of the wellbore. The resulting values can represent the baselinetemperature excursion feature.

In some embodiments, the temperature features can comprise apeak-to-peak temperature value. This feature can represent thedifference between the maximum and minimum values (e.g., the range,etc.) within the temperature profile along the wellbore 114. In someembodiments, the peak-to-peak temperature values can be determined bydetecting the maximum temperature readings (e.g., the peaks) and theminimum temperature values (e.g., the dips) within the temperatureprofile along the wellbore 114. The difference can then be determinedwithin the temperature profile to determine peak-to-peak values alongthe length of the wellbore 114. The resulting peak-to-peak values canthen be processed to determine a change in the peak-to-peak values withrespect to depth. In some embodiments, this can include taking aderivative of the peak-to-peak values with respect to depth along thelongitudinal axis of the wellbore 114. The resulting values canrepresent the peak-to-peak temperature values.

Other temperature features can also be determined from the temperaturemeasurements. In some embodiments, various statistical measurements canbe obtained from the temperature measurements along the wellbore 114 todetermine one or more temperature features. For example, across-correlation of the temperature measurements with respect to timecan be used to determine a cross-correlated temperature feature. Thetemperature measurements can be smoothed as described herein prior todetermining the cross-correlation with respect to time. As anotherexample, an autocorrelation measurement of the temperature measurementscan be obtained with respect to depth. Autocorrelation is defined as thecross-correlation of a signal with itself. An autocorrelationtemperature feature can thus measure the similarity of the signal withitself as a function of the displacement. An autocorrelation temperaturefeature can be used, in applications, as a means of anomaly detectionfor event (e.g., fluid inflow) detection. The temperature measurementscan be smoothed and/or the resulting autocorrelation measurements can besmoothed as described herein to determine the autocorrelationtemperature features.

In some embodiments, the temperature features can comprise a FastFourier transform (FFT) of the distributed temperature sensing (e.g.,DTS) signal. This algorithm can transform the distributed temperaturesensing signal from the time domain into the frequency domain, thusallowing detection of the deviation in DTS along length (e.g., depth).This temperature feature can be utilized, for example, for anomalydetection for event (e.g., fluid inflow) detection purposes.

In some embodiments, the temperature features can comprise the Laplacetransform of DTS. This algorithm can transform the DTS signal from thetime domain into Laplace domain allows us to detect the deviation in theDTS along length (e.g., depth of wellbore 114). This temperature featurecan be utilized, for example, for anomaly detection for event (e.g.,fluid inflow) detection. This feature can be utilized, for example, inaddition to (e.g., in combination with) the FFT temperature feature.

In some embodiments, the temperature features can comprise a wavelettransform of the distributed temperature sensing (e.g., DTS) signaland/or of the derivative of DTS with respect to depth, dT/dz. Thewavelet transform can be used to represent the abrupt changes in thesignal data. This feature can be utilized, for example, in inflowdetection. A wavelet is described as an oscillation that has zero mean,which can thus make the derivative of DTS in depth more suitable forthis application. In embodiments and without limitation, the wavelet cancomprise a Morse wavelet, an Analytical wavelet, a Bump wavelet, or acombination thereof.

In some embodiments, the temperature features can comprise a derivativeof DTS with respect to depth, or dT/dz. The relationship between thederivative of flowing temperature T_(f) with respect to depth (L) (e.g.,dT_(f)/dL) has been described by several models. For example, andwithout limitation, the model described by Sagar (Sagar, R., Doty, D.R., & Schmidt, Z. (1991, November 1). Predicting Temperature Profiles ina Flowing Well. Society of Petroleum Engineers. doi:10.2118/19702-PA)which accounts for radial heat loss due to conduction and describes arelationship (Equation (1) below) between temperature change in depthand mass rate. The mass rate w_(t) is conversely proportional to therelaxation parameter A and, as the relaxation parameter A increases, thechange in temperature in depth increases. Hence this temperature featurecan be designed to be used, for example, in events comprising inflowquantification.

The formula for the

$\begin{matrix}{{\frac{dT_{f}}{dL} = {- {A\left\lbrack {\left( {T_{f} - T_{e}} \right) + {\frac{g}{g_{c}}\frac{\sin\theta}{{JC}_{pm}A}} - \frac{F_{c}}{A}} \right\rbrack}}},} & (1)\end{matrix}$

relaxation parameter, A, is provided in Equation (2):

$\begin{matrix}{A = {\left( \frac{2\pi}{w_{i}C_{pl}} \right)\left( \frac{r_{ti}{Uk}_{e}}{k_{e} + {r_{ti}{Uf}/12}} \right)\left( \frac{1}{86{,400 \times 12}} \right)}} & (2)\end{matrix}$

-   -   A=coefficient, ft⁻¹    -   C_(pL)=specific heat of liquid, Btu/lbm-° F.    -   C_(pm)=specific heat of mixture, Btu/lbm-° F.    -   C_(po)=specific heat of oil, Btu/lbm-° F.    -   C_(pw)=specific heat of water, Btu/lbm-° F.    -   d_(c)=casing diameter, in.    -   d_(t)=tubing diameter, in.    -   d_(wb)=wellbore diameter, in.    -   D=depth, ft    -   D_(inj)=injection depth, ft    -   f=modified dimensionless heat conduction time function for tong        times for earth    -   f(t)=dimensionless transient heat conduction time function for        earth    -   F_(c)=correction factor    -   F _(c)=average correction factor for one length interval    -   g=acceleration of gravity, 32.2 ft/sec²    -   g_(c)=conversion factor, 32.2 ft-lbm/sec²-lbf    -   g_(G) geothermal gradient, ° F./ft    -   h=specific enthalpy, Btu/lbm    -   J=mechanical equivalent of heat, 778 ft-lbf/Btu    -   k_(an)=thermal conductivity of material in annulus, Btu/D-ft-°        F.    -   k_(ang)=thermal conductivity of gas in annulus, Btu/D-ft-° F.    -   k_(anw)=thermal conductivity of water in annulus, Btu/D-ft-° F.    -   k_(cem)=thermal conductivity of cement, Btu/D-ft-° F.    -   k_(c)=thermal conductivity of earth, Btu/D-ft-° F.    -   L=length of well from perforations, ft    -   L_(in)=length from perforation to inlet, ft    -   p=pressure, psi    -   p_(wh)=wellhead pressure, psig    -   q_(gf)=formation gas flow rate, scf/D    -   q_(ginj)=injection gas flow rate, scf/D    -   q_(o)=oil flow rate, STB/D    -   q_(w)=water flow rate, STB/D    -   Q=heat transfer between fluid and surrounding area, Btu/lbm    -   r_(ci)=inside casing radius, in.    -   r_(co)=outside casing radius, in.    -   r_(ri)=inside tubing radius, in.    -   r_(ro)=outside tubing radius, in.    -   r_(wh)=wellbore radius, in.    -   R_(gL)=gas/liquid ratio, scf/STB    -   T=temperature. ° F.    -   T_(bh)=bottomhole temperature, ° F.    -   T_(c)=casing temperature, ° F.    -   T_(e)=surrounding earth temperature, ° F.    -   T_(ein)=earth temperature at inlet, ° F.    -   T_(f)=flowing fluid temperature, ° F.    -   T_(f)=flowing fluid temperature at inlet, ° F.    -   T_(h) cement/earth interface temperature, ° F.    -   U=overall heat transfer coefficient, Btu/D-ft²-° F.    -   v=fluid velocity, ft/sec    -   V=volume    -   w_(t)=total mass flow rate, lbm/sec    -   Z=height from bottom of hole, ft    -   Z_(in)=height from bottom of hole at inlet, ft    -   α=thermal diffusivity of earth, 0.04 ft²/hr    -   γ_(API)=oil gravity, ° API    -   γ_(g)=gas specific gravity (air=1)    -   γ_(o)=oil specific gravity    -   γ_(w)=water specific gravity    -   θ=angle of inclination, degrees    -   μ=Joule-Thomson coefficient

In some embodiments, the temperature features can comprise a heat lossparameter. As described hereinabove, Sagar's model describes therelationship between various input parameters, including the mass ratew_(t) and temperature change in depth dT_(f)/dL. These parameters can beutilized as temperature features in a machine learning model which usesfeatures from known cases (production logging results) as learning datasets, when available. These features can include geothermal temperature,deviation, dimensions of the tubulars 120 that are in the well (casing112, tubing 120, gravel pack 122 components, etc.), as well as thewellbore 114, well head pressure, individual separator rates, downholepressure, gas/liquid ratio, and/or a combination thereof. Such heat lossparameters can, for example, be utilized as inputs in a machine learningmodel for events comprising inflow quantification of the mass flow ratew_(t).

In some embodiments, the temperature features can comprise a time-depthderivative and/or a depth-time derivative. A temperature featurecomprising a time-depth derivative can comprise a change in atemperature measurement at one or more locations across the wellboretaken first with respect to time, and a change in the resulting valueswith respect to depth can then be determined. Similarly, a temperaturefeature comprising a depth-time derivative can comprise a change in atemperature measurement at one or more locations across the wellboretaken first with respect to depth, and a change in the resulting valueswith respect to time can then be determined.

In some embodiments, the temperature features can be based on dynamictemperature measurements rather than steady state or flowing temperaturemeasurements. In order to obtain dynamic temperature measurements, achange in the operation of the system (e.g., wellbore) can beintroduced, and the temperature monitored using the temperaturemonitoring system. For example in a wellbore environment, the change inconditions can be introduced by shutting in the wellbore, opening one ormore sections of the wellbore to flow, introducing a fluid to thewellbore (e.g., injecting a fluid), and the like. When the wellbore isshut in from a flowing state, the temperature profile along the wellboremay be expected to change from the flowing profile to the baselineprofile over time. Similarly, when a wellbore that is shut in is openedfor flow, the temperature profile may change from a baseline profile toa flowing profile. Based on the change in the condition of the wellbore,the temperature measurements can change dynamically over time. In someembodiments, this approach can allow for a contrast in thermalconductivity to be determined between a location or interval havingradial flow (e.g., into or out of the wellbore) to a location orinterval without radial flow. One or more temperature features can thenbe determined using the dynamic temperature measurements. Once thetemperature features are determined from the temperature measurementsobtained from the temperature monitoring system, one or more of thetemperature features can be used to identify events along the lengthbeing monitored (e.g., within the wellbore), as described in more detailherein.

As described with respect to the temperature measurements, the inflow offluids into the wellbore 114 an also create acoustic sounds that can bedetected using the acoustic monitoring system such as a DAS system.Accordingly, the flow of the various fluids into the wellbore 114 and/orthrough the wellbore 114 can create vibrations or acoustic sounds thatcan be detected using acoustic monitoring system. Each type of inflowevent such as the different fluid flows and fluid flow locations canproduce an acoustic signature with unique frequency domain features.

As used herein, various frequency domain features can be obtained fromthe acoustic signal, and in some contexts, the frequency domain featurescan also be referred to herein as spectral features or spectraldescriptors. The frequency domain features are features obtained from afrequency domain analysis of the acoustic signals obtained within thewellbore. The frequency domain features can be derived from the fullspectrum of the frequency domain of the acoustic signal such that eachof the frequency domain features can be representative of the frequencyspectrum of the acoustic signal. Further, a plurality of differentfrequency domain features can be obtained from the same acoustic signal(e.g., the same acoustic signal at a location or depth within thewellbore), where each of the different frequency domain features isrepresentative of frequencies across the same frequency spectrum of theacoustic signal as the other frequency domain features. For example, thefrequency domain features (e.g., each frequency domain feature) can be astatistical shape measurement or spectral shape function of the spectralpower measurement across the same frequency bandwidth of the acousticsignal. Further, as used herein, frequency domain features can alsorefer to features or feature sets derived from one or more frequencydomain features, including combinations of features, mathematicalmodifications to the one or more frequency domain features, rates ofchange of the one or more frequency domain features, and the like.

The frequency domain features can be determined by processing theacoustic signals from within the wellbore at one or more locations alongthe wellbore. As the acoustics signals at a given location along thewellbore contain a combination of acoustic signals, the determination ofthe frequency domain features can be used to separate and identifyindividual fluid inflow events. As an example, FIG. 3 illustrates sand202 flowing from the formation 102 into the wellbore 114 and then intothe tubular 120. As the sand 202 flows into the tubular 120, it cancollide against the inner surface 204 of the tubular 120, and with thefiber 162 (e.g., in cases where the fiber 162 is placed within thetubular 120), in a random fashion. Without being limited by this or anyparticular theory, the intensity of the collisions depends on theeffective mass and the rate of change in the velocity of the impingingsand particles 202, which can depend on a number of factors including,without limitation, the direction of travel of the sand 202 in thewellbore 114 and/or tubular 120. The resulting random impacts canproduce a random, broadband acoustic signal that can be captured on theoptical fiber 162 coupled (e.g., strapped) to the tubular 120. Therandom excitation response tends to have a broadband acoustic signalwith excitation frequencies extending up to the high frequency bands,for example, up to and beyond about 5 kHz depending on the size of thesand particles 202. In general, larger particle sizes may produce higherfrequencies. The intensity of the acoustic signal may be proportional tothe concentration of sand 202 generating the excitations such that anincreased broad band power intensity can be expected at increasing sand202 concentrations. In some embodiments, the resulting broadbandacoustic signals that can be identified can include frequencies in therange of about 5 Hz to about 10 kHz, frequencies in the range of about 5Hz to about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in therange of about 500 Hz to about 5 kHz. Any frequency ranges between thelower frequencies values (e.g., 5 Hz, 50 Hz, 500 Hz, etc.) and the upperfrequency values (e.g., 10 kHz, 7 kHz, 5 kHz, etc.) can be used todefine the frequency range for a broadband acoustic signal.

In addition to the sand entering the wellbore, fluid inflow at thelocation can also create acoustic signals along with fluid flowingaxially or longitudinally through the wellbore. Background noise canalso be present. Other acoustic signal sources can include fluid flowwith or without sand 202 through the formation 102, fluid flow with orwithout sand 202 through a gravel pack 122, fluid flow with or withoutsand 202 within or through the tubular 120 and/or sand screen 118, fluidflow with sand 202 within or through the tubular 120 and/or sand screen118, fluid flow without sand 202 into the tubular 120 and/or sand screen118, gas/liquid inflow, hydraulic fracturing, fluid leaks pastrestrictions (e.g., gas leaks, liquid leaks, etc.) mechanicalinstrumentation and geophysical acoustic noises and potential pointreflection noise within the fiber caused by cracks in the fiber opticcable/conduit under investigation. The combined acoustic signal can thenbe detected by the acoustic monitoring system. In order to detect one ormore of these events, the acoustic signal can be processed to determineone or more frequency domain features of the acoustic signal at a depthin the wellbore.

In order to determine the frequency domain features, an acoustic signalcan be obtained using the acoustic monitoring system during operation ofthe wellbore. The resulting acoustic signal can be optionallypre-processed using a number of steps. Depending on the type of DASsystem employed, the optical data may or may not be phase coherent andmay be pre-processed to improve the signal quality (e.g., denoised foropto-electronic noise normalization/de-trending single point-reflectionnoise removal through the use of median filtering techniques or eventhrough the use of spatial moving average computations with averagingwindows set to the spatial resolution of the acquisition unit, etc.).The raw optical data from the acoustic sensor can be received,processed, and generated by the sensor to produce the acoustic signal.

In some embodiments, a processor or collection of processors (e.g.,processor 168 in FIG. 1 ) may be utilized to perform the optionalpre-processing steps described herein. In an embodiment, the noisedetrended “acoustic variant” data can be subjected to an optionalspatial filtering step following the other pre-processing steps, ifpresent. A spatial sample point filter can be applied that uses a filterto obtain a portion of the acoustic signal corresponding to a desireddepth or depth range in the wellbore. Since the time the light pulsesent into the optical fiber returns as backscattered light cancorrespond to the travel distance, and therefore depth in the wellbore,the acoustic data can be processed to obtain a sample indicative of thedesired depth or depth range. This may allow a specific location withinthe wellbore to be isolated for further analysis. The pre-processing mayalso include removal of spurious back reflection type noises at specificdepths through spatial median filtering or spatial averaging techniques.This is an optional step and helps focus primarily on an interval ofinterest in the wellbore. For example, the spatial filtering step can beused to focus on a producing interval where there is high likelihood ofsand ingress, for example. The resulting data set produced through theconversion of the raw optical data can be referred to as the acousticsample data.

The acoustic data, including the optionally pre-processed and/orfiltered data, can be transformed from the time domain into thefrequency domain using a transform. For example, a Fourier transformsuch as a Discrete Fourier transformations (DFT), a short time Fouriertransform (STFT), or the like can be used to transform the acoustic datameasured at each depth section along the fiber or a section thereof intoa frequency domain representation of the signal. The resulting frequencydomain representation of the data can then be used to provide the datafrom which the plurality of frequency domain features can be determined.Spectral feature extraction using the frequency domain features throughtime and space can be used to determine one or more frequency domainfeatures.

The use of frequency domain features to identify inflow events andlocations, inflow phase identification, and/or inflow quantities of oneor more fluid phases can provide a number of advantages. First, the useof frequency domain features results in significant data reductionrelative to the raw DAS data stream. Thus, a number of frequency domainfeatures can be calculated and used to allow for event identificationwhile the remaining data can be discarded or otherwise stored, and theremaining analysis can performed using the frequency domain features.Even when the raw DAS data is stored, the remaining processing power issignificantly reduced through the use of the frequency domain featuresrather than the raw acoustic data itself. Further, the use of thefrequency domain features can, with the appropriate selection of one ormore of the frequency domain features, provide a concise, quantitativemeasure of the spectral character or acoustic signature of specificsounds pertinent to downhole fluid surveillance and other applications.

While a number of frequency domain features can be determined for theacoustic sample data, not every frequency domain feature may be used toidentify inflow events and locations, inflow phase identification,and/or inflow quantities of one or more fluid phases. The frequencydomain features represent specific properties or characteristics of theacoustic signals.

In some embodiments, combinations of frequency domain features can beused as the frequency domain features themselves, and the resultingcombinations are considered to be part of the frequency domain featuresas described herein. In some embodiments, a plurality of frequencydomain features can be transformed to create values that can be used todefine various event signatures. This can include mathematicaltransformations including ratios, equations, rates of change, transforms(e.g., wavelets, Fourier transforms, other wave form transforms, etc.),other features derived from the feature set, and/or the like as well asthe use of various equations that can define lines, surfaces, volumes,or multi-variable envelopes. The transformation can use othermeasurements or values outside of the frequency domain features as partof the transformation. For example, time domain features, other acousticfeatures, and non-acoustic measurements can also be used. In this typeof analysis, time can also be considered as a factor in addition to thefrequency domain features themselves. As an example, a plurality offrequency domain features can be used to define a surface (e.g., aplane, a three-dimensional surface, etc.) in a multivariable space, andthe measured frequency domain features can then be used to determine ifthe specific readings from an acoustic sample fall above or below thesurface. The positioning of the readings relative to the surface canthen be used to determine if the event is present or not at thatlocation in that detected acoustic sample.

The frequency domain features can include any frequency domain featuresderived from the frequency domain representations of the acoustic data.Such frequency domain features can include, but are not limited to, thespectral centroid, the spectral spread, the spectral roll-off, thespectral skewness, the root mean square (RMS) band energy (or thenormalized sub-band energies/band energy ratios), a loudness or totalRMS energy, a spectral flatness, a spectral slope, a spectral kurtosis,a spectral flux, a spectral autocorrelation function, or a normalizedvariant thereof.

The spectral centroid denotes the “brightness” of the sound captured bythe optical fiber (e.g., optical fiber 162 shown in FIG. 1 ) andindicates the center of gravity of the frequency spectrum in theacoustic sample. The spectral centroid can be calculated as the weightedmean of the frequencies present in the signal, where the magnitudes ofthe frequencies present can be used as their weights in someembodiments.

The spectral spread is a measure of the shape of the spectrum and helpsmeasure how the spectrum is distributed around the spectral centroid. Inorder to compute the spectral spread, Si, one has to take the deviationof the spectrum from the computed centroid as per the following equation(all other terms defined above):

$\begin{matrix}{S_{i} = {\sqrt{\frac{{\Sigma_{k = 1}^{N}\left( {{f(k)} - C_{i}} \right)}^{2}{X_{i}(k)}}{\Sigma_{k = 1}^{N}{X_{i}(k)}}}.}} & \left( {{Eq}.2} \right)\end{matrix}$

The spectral roll-off is a measure of the bandwidth of the audio signal.The Spectral roll-off of the i^(th) frame, is defined as the frequencybin ‘y’ below which the accumulated magnitudes of the short-time Fouriertransform reach a certain percentage value (usually between 85%-95%) ofthe overall sum of magnitudes of the spectrum.

$\begin{matrix}{{{\sum_{k = 1}^{y}\left| {X_{i}(k)} \right|} = \left. {\frac{c}{100}\sum_{k = 1}^{N}} \middle| {X_{i}(k)} \right|},} & \left( {{Eq}.3} \right)\end{matrix}$

where c=85 or 95. The result of the spectral roll-off calculation is abin index and enables distinguishing acoustic events based on dominantenergy contributions in the frequency domain (e.g., between gas influxand liquid flow, etc.).

The spectral skewness measures the symmetry of the distribution of thespectral magnitude values around their arithmetic mean.

The RMS band energy provides a measure of the signal energy withindefined frequency bins that may then be used for signal amplitudepopulation. The selection of the bandwidths can be based on thecharacteristics of the captured acoustic signal. In some embodiments, asub-band energy ratio representing the ratio of the upper frequency inthe selected band to the lower frequency in the selected band can rangebetween about 1.5:1 to about 3:1. In some embodiments, the sub-bandenergy ratio can range from about 2.5:1 to about 1.8:1, or alternativelybe about 2:1 The total RMS energy of the acoustic waveform calculated inthe time domain can indicate the loudness of the acoustic signal. Insome embodiments, the total RMS energy can also be extracted from thetemporal domain after filtering the signal for noise.

The spectral flatness is a measure of the noisiness/tonality of anacoustic spectrum. It can be computed by the ratio of the geometric meanto the arithmetic mean of the energy spectrum value and may be used asan alternative approach to detect broad-banded signals. For tonalsignals, the spectral flatness can be close to 0 and for broader bandsignals it can be closer to 1.

The spectral slope provides a basic approximation of the spectrum shapeby a linearly regressed line. The spectral slope represents the decreaseof the spectral amplitudes from low to high frequencies (e.g., aspectral tilt). The slope, the y-intersection, and the max and mediaregression error may be used as features.

The spectral kurtosis provides a measure of the flatness of adistribution around the mean value.

The spectral flux is a measure of instantaneous changes in the magnitudeof a spectrum. It provides a measure of the frame-to-frame squareddifference of the spectral magnitude vector summed across allfrequencies or a selected portion of the spectrum. Signals with slowlyvarying (or nearly constant) spectral properties (e.g., noise) have alow spectral flux, while signals with abrupt spectral changes have ahigh spectral flux. The spectral flux can allow for a direct measure ofthe local spectral rate of change and consequently serves as an eventdetection scheme that could be used to pick up the onset of acousticevents that may then be further analyzed using the feature set above toidentify and uniquely classify the acoustic signal.

The spectral autocorrelation function provides a method in which thesignal is shifted, and for each signal shift (lag) the correlation orthe resemblance of the shifted signal with the original one is computed.This enables computation of the fundamental period by choosing the lag,for which the signal best resembles itself, for example, where theautocorrelation is maximized. This can be useful in exploratorysignature analysis/even for anomaly detection for well integritymonitoring across specific depths where well barrier elements to bemonitored are positioned.

Any of these frequency domain features, or any combination of thesefrequency domain features (including transformations of any of thefrequency domain features and combinations thereof), can be used todetect inflow events and locations, inflow phase identification, and/orinflow quantities of one or more phases within the wellbore. In anembodiment, a selected set of characteristics can be used to identifythe presence or absence for each event, and/or all of the frequencydomain features that are calculated can be used as a group incharacterizing the presence or absence of an event. The specific valuesfor the frequency domain features that are calculated can vary dependingon the specific attributes of the acoustic signal acquisition system,such that the absolute value of each frequency domain feature can changebetween systems. In some embodiments, the frequency domain features canbe calculated for each event based on the system being used to capturethe acoustic signal and/or the differences between systems can be takeninto account in determining the frequency domain feature values for eachfluid inflow event between or among the systems used to determine thevalues and the systems used to capture the acoustic signal beingevaluated.

One or a plurality of frequency domain features can be used to identifyinflow events and locations, inflow phase identification, and/or inflowquantities of one or more phases. In some embodiments, one or aplurality of frequency domain features can also be used to detect inflowevents and locations, inflow phase identification, and/or inflowquantities of one or more phases. In an embodiment, one, or at leasttwo, three, four, five, six, seven, eight, etc. different frequencydomain features can be used to detect inflow events and locations,inflow phase identification, and/or inflow quantities of one or morephases. The frequency domain features can be combined or transformed inorder to define the event signatures for one or more events, such as,for instance, a fluid inflow event location or flowrate. While exemplarynumerical ranges are provided herein, the actual numerical results mayvary depending on the data acquisition system and/or the values can benormalized or otherwise processed to provide different results.

The systems described herein can be used with the temperature featuresand/or frequency domain features to determine the presence and/or extentof an event at one or more locations. FIG. 4 illustrates a method 400for detecting an event at one or more locations. The method can start atstep 402 with a determination of temperature features and acousticfeatures originating from the event (e.g., within the wellbore). Forexample, for an event comprising fluid inflow, one or more fluids thatcan include gas, a liquid aqueous phase, a liquid hydrocarbon phase, andpotentially other fluids as well as various combinations thereof canenter the wellbore at one or more locations along the wellbore. Thetemperature features can then be used to identify these inflowlocations. Similarly, the temperature features can be utilized todetermine the presence or absence of an event at one or more locations(e.g., along a length, such as, without limitation, along a pipeline, asecurity perimeter, an apparatus, a dam, etc.). As noted at step 404 anddetailed further hereinbelow, the temperature features can be utilizedwith a first or event identification/detection model to provide anoutput of the first model and the be utilized with a second or eventextent model to provide an output of the second model. As noted at step406 and detailed further hereinbelow, the presence and/or extent of theevent at the one or more locations can be determined using the outputfrom the first or event identification/detection model, the output fromthe second or event extent model, or a combined output obtained usingthe output from the first or event identification model and the outputfrom the second or event extent model. Although described as an “eventextent” model, the second model can also be utilized as a (second) eventidentification/detection model to determine the presence or absence ofthe event at one or more locations along the length being monitored.

The temperature features can be determined using the temperaturemonitoring system to obtain temperature measurements along the lengthbeing monitored (e.g., the length of the wellbore). In some embodiments,a DTS system can be used to receive distributed temperature measurementsignals from a sensor disposed along the length (e.g., the length of thewellbore), such as an optical fiber. The resulting signals from thetemperature monitoring system can be used to determine one or moretemperature features as described herein. In some embodiments, abaseline or background temperature profile can be used to determine thetemperature features, and the baseline temperature profile can beobtained prior to obtaining the temperature measurements.

In some embodiments, a plurality of temperature features can bedetermined from the temperature measurements, and the plurality oftemperature features can comprise at least two of: a depth derivative oftemperature with respect to depth, a temperature excursion measurement,a baseline temperature excursion, a peak-to-peak value, a fast Fouriertransform, a Laplace transform, a wavelet transform, a derivative oftemperature with respect to length (e.g., depth), a heat loss parameter,an autocorrelation, as detailed hereinabove, and/or the like. Othertemperature features can also be used in some embodiments. Thetemperature excursion measurement can comprise a difference between atemperature reading at a first depth, and a smoothed temperature readingover a depth range, where the first depth is within the depth range. Thebaseline temperature excursion can comprise a derivative of a baselineexcursion with depth, where the baseline excursion can comprise adifference between a baseline temperature profile and a smoothedtemperature profile. The peak-to-peak value can comprise a derivative ofa peak-to-peak difference with depth, where the peak-to-peak differencecomprises a difference between a peak high temperature reading and apeak low temperature reading with an interval. The fast FourierTransform can comprise an FFT of the distributed temperature sensingsignal. The Laplace transform can comprise a Laplace transform of thedistributed temperature sensing signal. The wavelet transform cancomprise a wavelet transform of the distributed temperature sensingsignal or of the derivative of the distributed temperature sensingsignal with respect to length (e.g., depth). The derivative of thedistributed temperature sensing signal with respect to length (e.g.,depth) can comprise the derivative of the flowing temperature withrespect to depth. The heat loss parameter can comprise one or more ofthe geothermal temperature, a deviation, dimensions of the tubulars thatare in the well, well head pressure, individual separator rates,downhole pressure, gas/liquid ratio, or the like. The autocorrelationcan comprise a cross-correlation of the distributed temperature sensingsignal with itself.

Once the temperature features are obtained, the temperature features canbe used with a first or event (e.g., fluid inflow) identification modelto identify the presence of the event at one or more locations. In someembodiments, the first or event identification model can accept aplurality of temperature features as inputs. In general, the temperaturefeatures are representative of feature at a particular location (e.g., adepth resolution portion of the optical fiber along the length beingmonitored, e.g., along a length of the wellbore) along the length. Thefirst or event identification model can comprise one or more modelsconfigured to accept the temperature features as input(s) and provide anindication of whether or not there is an event at the particularlocation along the length. The output of the first or eventidentification model can be in the form of a binary yes/no result,and/or a likelihood of an event (e.g., a percentage likelihood, etc.).Other outputs providing an indication of an event are also possible. Insome embodiments, the first or event identification model can comprise amultivariate model, a machine learning model using supervised orunsupervised learning algorithms, or the like.

In some embodiments, the first or event identification model cancomprise a multivariate model. A multivariate model allows for the useof a plurality of variables in a model to determine or predict anoutcome. A multivariate model can be developed using known data onevents along with temperature features for those events to develop arelationship between the temperature features and the presence of theevent at the locations within the available data. One or moremultivariate models can be developed using data, where each multivariatemodel uses a plurality of temperature features as inputs to determinethe likelihood of an event occurring at the particular location alongthe length.

In some embodiments, the first or event identification model cancomprise one or more multivariate models. The multivariate model can usemultivariate equations, and the multivariate model equations can use thetemperature features or combinations or transformations thereof todetermine when an event is present. The multivariate model can define athreshold, decision point, and/or decision boundary having any type ofshapes such as a point, line, surface, or envelope between the presenceand absence of the specific event. In some embodiments, the multivariatemodel can be in the form of a polynomial, though other representationsare also possible. The model can include coefficients that can becalibrated based on known event data. While there can be variability oruncertainty in the resulting values used in the model, the uncertaintycan be taken into account in the output of the model. Once calibrated ortuned, the model can then be used with the corresponding temperaturefeatures to provide an output that is indicative of the occurrence of anevent.

The multivariate model is not limited to two dimensions (e.g., twotemperature features or two variables representing transformed valuesfrom two or more temperature features), and rather can have any numberof variables or dimensions in defining the threshold between thepresence or absence of the event. When used, the detected values can beused in the multivariate model, and the calculated value can be comparedto the model values. The presence of the event can be indicated when thecalculated value is on one side of the threshold and the absence of theevent can be indicated when the calculated value is on the other side ofthe threshold. In some embodiments, the output of the multivariate modelcan be based on a value from the model relative to a normal distributionfor the model. Thus, the model can represent a distribution or envelopeand the resulting temperature features can be used to define where theoutput of the model lies along the distribution at the location alongthe length being monitored (e.g., along the length of the wellbore).Thus, each multivariate model can, in some embodiments, represent aspecific determination between the presence of absence of an event at aspecific location along the length. Different multivariate models, andtherefore thresholds, can be used for different events, and eachmultivariate model can rely on different temperature features orcombinations or transformations of temperature features. Since themultivariate models define thresholds for the determination and/oridentification of events, the multivariate models and first or eventidentification model using such multivariate models can be considered tobe temperature based event signatures for each type of event.

In some embodiments, the first or event identification model cancomprise a plurality of models. Each of the models can use one or moreof the temperature features as inputs. The models can comprise anysuitable model that can relate one or more temperature features to anoccurrence of an event (e.g., a likelihood of the event, a binary yes/nooutput, etc.). The output of each model can then be combined to form acomposite or combined output. The combined output can then be used todetermine if an event has occurred, for example, by comparing thecombined output with a threshold value (e.g., a fluid inflow threshold).The determination of the occurrence of an event can then be based on thecomparison of the combined output with the threshold value.

As an example, the first or event identification model can comprise aplurality of multivariate models, each using a plurality of temperaturefeatures as described above. The output of the multivariate models caninclude a percentage likelihood of the occurrence of an event at theparticular location at which each model is applied. The resulting outputvalues can then be used in a function such as a simple multiplication, aweighted average, a voting scheme, or the like to provide a combinedoutput. The resulting output can then be compared to a threshold todetermine if an event has occurred. For example, a combined outputindicating that there is greater than a fifty percent likelihood of anevent at the particular location can be taken as an indication that theevent has occurred at the location of interest.

In some embodiments, the first or event identification model can alsocomprise other types of models. In some embodiments, a machine learningapproach comprises a logistic regression model. In some suchembodiments, one or more temperature features can be used to determineif an event is present at one or more locations of interest. The machinelearning approach can rely on a training data set that can be obtainedfrom a test set-up or obtained based on actual temperature data fromknown events. The one or more temperature features in the training dataset can then be used to train the first or event identification modelusing machine learning, including any supervised or unsupervisedlearning approach. For example, the first or event identification modelcan be a neural network, a Bayesian network, a decision tree, alogistical regression model, a normalized logistical regression model,or the like. In some embodiments, the first or event identificationmodel can comprise a model developed using unsupervised learningtechniques such a k-means clustering and the like.

In some embodiments, the model(s) can be developed and trained using alogistic regression model. As an example for training of a model used todetermine the presence or absence of an event, the training of the modelcan begin with providing the one or more temperature features to thelogistic regression model corresponding to one or more reference datasets in which event(s) are present. Additional reference data sets canbe provided in which event(s) are not present. The one or moretemperature features can be provided to the logistic regression model,and a first multivariate model can be determined using the one or morefrequency domain features as inputs. The first multivariate model candefine a relationship between a presence and an absence of the events.

Once the model is trained, the first or event identification model canbe used to determine the presence or absence of an event at one or morelocations along the length (e.g., the length of the wellbore) in step406. The temperature features determined for each location along thelength can be used with the first or event identification model. Theoutput of the first or event identification model can provide anindication of the presence of an event at each location for which thetemperature features are obtained. When the output indicates that anevent has occurred at a given location, an output can be generatedindicating the presence of the event. The process can be repeated alongthe length to provide an event profile, which can comprise an indicationof the events at one or more locations along the length being monitored.

In some embodiments, the event outputs from the first or eventidentification model can be presented as a profile along a length on anoutput device. The outputs can be presented in the form of an eventprofile depicted along an axis with or without a schematic. The eventprofile can then be used to visualize the event locations, which canallow for various processes to be carried out. For example, for eventscomprising inflow, the fluid inflow locations can be compared to theproducing zones within a completion to understand where fluid isentering the wellbore. In some embodiments, fluid inflow can be detectedat locations other than a producing zone, which may provide anindication that a remediation procedure is needed within the wellbore.

Also disclosed herein is a process for validating the event locationsfrom the first or event identification model using the temperaturefeatures and/or determining an extent of the event (e.g., a quantity offluid (e.g., a liquid) entering the wellbore, blockage within apipeline, etc.) at the one or more event locations identified by thefirst or event identification model. The second or event extent modelcan use one or more frequency domain features in one or more eventmodels to predict an extent of the event(s) (e.g., a quantity or flowrate of one or more fluids and/or fluid phases into the wellbore, amountof leakage from a pipeline, etc.). For example, when the event comprisesfluid inflow in a wellbore, the second or event extent model can be usedto predict the inflow rates of one or more fluids including a gas, anaqueous liquid, a hydrocarbon liquid, or another fluid within thewellbore. In some embodiments, the second or event extent model can beused to predict the inflow rate of a fluid phase such as a gas phaseand/or a liquid phase (e.g., including a liquid aqueous phase and ahydrocarbon liquid phase).

In some embodiments, the frequency domain features can be used with asecond or event extent model to predict a fluid inflow rate, such as aliquid flowrate into the wellbore. The second or event extent model canrelate a fluid inflow rate of one or more phases (e.g., including atotal liquid flow rate) to one or more frequency domain features. Insome embodiments, the second or event extent model can accept one ormore frequency domain features as inputs. In general, the frequencydomain features are representative of feature at a particular location(e.g., a depth resolution portion of the optical fiber along the length,e.g., the length of the wellbore) along the length. The second model cancomprise one or more models configured to accept the frequency domainfeatures as input(s) and provide an indication of an extent of the event(e.g., a fluid inflow rate) at the location. When the event comprisesfluid inflow, for example, the output of the second or event extentmodel can be, for example, in the form of a flow rate of one or morefluids and/or fluid phases. In some embodiments, the second or eventextent model can comprise a multivariate model, a machine learning modelusing supervised or unsupervised learning algorithms, or the like.

In some embodiments, a second or event extent model can be developedusing a machine learning approach. In some such embodiments, a singlefrequency domain feature (e.g., spectral flatness, RMS bin values, etc.)can be used to determine if the event is present at each location ofinterest. In some embodiments, the supervised learning approach can beused to determine a model of the event extent (e.g., inflow rate of oneor more fluids and/or fluid phases, such as gas inflow rate, ahydrocarbon inflow rate, a water inflow rate, a total gas phase inflowrate, and/or a total liquid phase (e.g., a liquid aqueous phase and aliquid hydrocarbon phase) inflow rate).

In some embodiments, the second or event extent model can be trainedusing a labeled data set, which can be obtained using a test apparatussuch as a test flow set-up and/or field data that is labeled using otherinstrumentation to identify the extent of an event. Using testing dataas an example, the method of developing the second or event extent modelcan include determining one or more frequency domain features from theacoustic signal for at least a portion of the data from the plurality oftests. The one or more frequency domain features can be obtained acrossthe portion of length where event occurs. The second or event extentmodel can then be trained using the frequency domain features from thetests. The training of the second or event extent model can use machinelearning, including any supervised or unsupervised learning approach.For example, the second or event extent model can be a neural network, aBayesian network, a decision tree, a logistical regression model, anormalized logistical regression model, k-means clustering or the like.

In some embodiments, the second or event extent model can be developedand trained using a logistic regression model. As an example fortraining of a model used to determine the extent of an event comprisingfluid inflow (e.g., to determine the fluid inflow rate), the training ofthe second or event extent model can begin with providing one or morefrequency domain features to the logistic regression model correspondingto one or more event tests where known event extents have been measured.Similarly, one or more frequency domain features can be provided to thelogistic regression model corresponding to one or more tests where noevent is present. A first multivariate model can be determined using theone or more frequency domain features as inputs. The first multivariatemodel can define a relationship between a presence and an absence of theevent and/or event extent.

In the second or event extent model, the multivariate model equationscan use the frequency domain features or combinations or transformationsthereof to determine when a specific event extent (e.g., a specificfluid inflow rate or fluid inflow rate for a fluid phase) is present.The multivariate model can define a threshold, decision point, and/ordecision boundary having any type of shapes such as a point, line,surface, or envelope between the presence and absence of the eventextent (e.g., the specific fluid inflow rate or fluid inflow rate for aphase). In some embodiments, the multivariate model can be in the formof a polynomial, though other representations are also possible. Whenmodels such a neural networks are used, the thresholds can be based onnode thresholds within the model. As noted herein, the multivariatemodel is not limited to two dimensions (e.g., two frequency domainfeatures or two variables representing transformed values from two ormore frequency domain features), and rather can have any number ofvariables or dimensions in defining the threshold between the presenceor absence of the event (e.g., fluid inflow) and the specific eventextents (e.g., fluid inflow rates for one or more fluids and/or fluidphases). Different multivariate models can be used for various eventextents (e.g., inflow rate for each fluid type and/or fluid inflowphase), and each multivariate model can rely on different frequencydomain features or combinations or transformations of frequency domainfeatures.

Whether a test system or in-situ sensors are used to obtain data on theevent extents (e.g., inflow rates), collectively referred to as“reference data”, one or more models can be developed for thedetermination of the event extents (e.g., inflow rates) using thereference data. The model(s) can be developed by determining one or morefrequency domain features from the acoustic signal for at least aportion of the reference data. The training of the model(s) can usemachine learning, including any supervised or unsupervised learningapproach. For example, one or more of the model(s) can be a neuralnetwork, a Bayesian network, a decision tree, a logistical regressionmodel, a normalized logistical regression model, k-means clustering, orthe like.

The one or more frequency domain features used in the second or eventextent model can comprise any frequency domain features notedhereinabove as well as combinations and transformations thereof. Forexample, In some embodiments, the one or more frequency domain featurescomprise a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, aspectral autocorrelation function, combinations and/or transformationsthereof, or any normalized variant thereof. In some embodiments, the oneor more frequency domain features comprise a normalized variant of thespectral spread (NVSS) and/or a normalized variant of the spectralcentroid (NVSC).

The output of the second or event extent model can comprise anindication of the event extent (e.g., the flow rate of one or morefluids and/or fluid phases). For example, for events comprising fluidinflow, the total liquid inflow rate at a location can be determinedfrom the second or event extent model. The resulting output can becompared to the output of the first or event identification model toallow the event (e.g., fluid inflow) location determination to be basedboth on the first or event identification model using the temperaturefeatures and the second or event extent model using the frequency domainfeatures. The final output can be a function of both the output from thefirst or event identification model and the second or event extentmodel. In some embodiments, the outputs can be combined as a product,weighted product, ratio, or other mathematical combination. Othercombinations can include voting schemes, thresholds, or the like toallow the outputs from both models to be combined. As an example, if theoutput from either model is zero, then the event identification at thelocation would also indicate that there is no event at the location. Inthis example, one model can indicate that an event is present, but theother model can indicate that no event is present. The final result canindicate that no event is present. When both models indicate that theevent is present, the final combined output can provide a positiveindication of the event at the location. It is noted that the output ofthe second or event extent model can provide one or more indications ofevent extents (e.g., a fluid inflow rate of one or more fluids and/orfluid phases). While this output is distinct from the output of thefirst or event identification model, the two outputs can be combined toimprove the accuracy of the event location identification.

Also described herein are methods and systems for using the combined orhybrid approach to determining event extents (e.g., fluid inflow rates)at the one or more locations at which an event (e.g., fluid inflow) isidentified. In these embodiments, the outputs of the first or eventidentification model and the second or event extent model can be usedtogether to help to determine an event extent (e.g., an inflow rate ofone or more fluids and/or fluid phases) along the length being monitored(e.g., within the wellbore). In some embodiments, the outputs of the twomodels can be combined to form a final event presence and/or eventextent determination. In some embodiments, the first or eventidentification model can be used to identify the one or more locationsat which the event (e.g., fluid inflow) is occurring, and the second orevent extent model can then be used to determine the event extent (e.g.,fluid inflow rates) at the identified locations, which can occur withoutcombining the outputs of the two models.

FIG. 5 illustrates a flow chart for a method 500 of determining thepresence and/or extent of an event. At step 502, the temperaturefeatures can be determined using any of the processes and systems asdescribed herein. In some embodiments, a DTS system can be used toobtain distributed temperature sensing signal along the length beingmonitored (e.g., along a length within the wellbore). The DTS system canprovide distributed temperature measurements along the length over time.A baseline temperature can be stored for the length as described hereinand used along with the temperature measurements to determine thetemperature features. The temperature features can include any of thosedescribed herein including a depth derivative of temperature withrespect to depth, a temperature excursion measurement, a baselinetemperature excursion, a peak-to-peak value, a fast Fourier transform, aLaplace transform, a wavelet transform, a derivative of temperature withrespect to length (e.g., depth), a heat loss parameter, anautocorrelation, a statistical measure of a variation with respect totime and/or distance, as detailed hereinabove, or a combination thereof.

At step 504, one or more frequency domain features can be obtained froman acoustic signal originating along the length being monitored (e.g.,within the wellbore). The frequency domain features can be determinedusing any of the processes and systems as described herein. In someembodiments, a DAS system can be used to obtain a distributed acousticsignal along the length being monitored (e.g., within the wellbore). Theacoustic signals obtained from the DAS system can then be processed todetermine one or more frequency domain features as described herein. Insome embodiments, the frequency domain features can comprise at leastone of: a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, aspectral autocorrelation function, or any combination thereof, includingcombinations and modifications thereof.

The temperature features and the frequency domain features can then beused to determine a presence and/or extent of one or more events (e.g.,a fluid inflow at one or more locations in a wellbore and/or a fluidinflow rate thereof) at one or more locations along the length beingmonitored in step 506. The temperature features and the frequency domainfeatures can be used in several ways to determine the presence and/orthe extent of the one or more events along the length being monitored.In some embodiments, the temperature features can be used in the firstor event identification model to obtain an identification of one or morelocations along the length having the event. Any of the models andmethods of using the temperature features within the models as describedherein can be used in step 406 to determine the one or more event (e.g.,fluid inflow) locations. The output of the first or event identificationmodel can provide an indication of one or more locations along thelength being monitored (e.g., along the length of the wellbore) havingan event.

The frequency domain features can be used in the second or event extentmodel to obtain an indication of the event extent (e.g., fluid inflowrate for one or more fluids and/or fluid phases) at the one or morelocations along the length (e.g., the wellbore). In some embodiments,the second or event extent model can be limited to being executed at theone or more locations identified by the first or event identificationmodel. The second or event extent model can then predict the eventextent(s) (e.g., fluid inflow rates of one or more fluids and/or fluidphases) at the one or more locations. The event extent(s) (e.g., fluidinflow rates) can then be representative of the event extents at the oneor more locations along the length (e.g., the wellbore).

In some embodiments, the output of the first or event identificationmodel and the second or event extent model can be combined to provide acombined output from the first or event identification model and thesecond or event extent model. The resulting combined output can then beused to determine an event extent (e.g., a fluid inflow rate) at the oneor more locations along the length being monitored (e.g., the wellbore)as identified by the first or event identification model. The combinedoutput can be determined as a function of the output of the first orevent identification model and the output of the second or event extentmodel. Any suitable functions can be used to combine the outputs of thetwo models. This can include formulas, products, averages, and the like,each of which can comprise one or more constants or weightings toprovide the final output. The ability to determine the event extent(s)as a function of the output of both models can allow for either model tooverride the output of the other model. For example, if the first orevent identification model indicates that a location along the lengthbeing monitored has an event, but the second or event extent modelindicates no event, the resulting combined output may be considered toindicate that there is no event at that location. Similarly, if thefirst or event identification model indicates a non-zero but lowlikelihood of an event at a location, the output can serve as aweighting to any event extents determined by the second or event extentmodel. Thus, the use of the hybrid model approach can provide twoseparate ways to verify and determine the event extents along the length(e.g., fluid inflow rates into the wellbore).

In some embodiments, both the temperature features and the frequencydomain features can then be used in a single model to determine apresence and/or extent of one or more events at one or more locationsalong the length being monitored in step 506. The temperature featuresand the frequency domain features can be used in an event identificationmodel as inputs to obtain an identification of one or more locationsalong the length having the event. In this embodiment, the test data forboth temperature features and frequency domain features can be used totrain a model using any of the models and techniques described herein.Once trained using one or more temperature features and one or morefrequency domain features, the resulting features determined for a timeinterval can be used as inputs into the model. The output of the eventidentification model can provide an indication of one or more locationsalong the length being monitored (e.g., along the length of thewellbore) having an event. The same model or a second model can then beused to obtain an indication of the event extent (e.g., fluid inflowrate for one or more fluids and/or fluid phases) at the one or morelocations along the length (e.g., the wellbore). The second model can beseparate in some embodiments. In other embodiments, the first model mayprovide an output including both the locations of one or more events andthe extent of such events.

The resulting output of the models can be an indication of an event atone or more locations along the length. The event prediction can be forone or more events (e.g., one or more fluids (e.g., a gas, an aqueousliquid, a hydrocarbon liquid, etc.) and/or a fluid phase (e.g., a gasphase, a liquid phase, etc.)). The event extents can be used asindicated by the model in their form as output by the model. In someembodiments, the total event extents can be normalized across the one ormore locations having the event. This can allow for a determination of arelative proportion of the event at each of the identified locations.This can be useful for understanding where the contributions to an eventare occurring along the length, irrespective of the absolute eventextent along the length.

In some embodiments, the event extents can be refined by using anindependent measure of the event extent (e.g., fluid flow rate from thewellbore as measured at logging tool above the producing zones, awellhead, surface flow line, or the like). Thus, as depicted in FIG. 5 ,method 500 can further comprise optional step 508 of independentlymeasuring an event extent. For example, when the event comprises fluidinflow and the event extent comprises the fluid inflow rate, the fluidproduction rate can be measured by a standard fluid flowrate measurementtool that is not associated with the acoustic monitoring system or thetemperature monitoring system within the wellbore. For example, thefluid production rate can be measured with various flow meters. Thefluid production rate can comprise an indication of the fluid flow ratesof one or more fluids and/or one or more fluid phases. The resultingevent extent (e.g., fluid production rate) information can then becombined with the output of the combined models as described herein. Insome embodiments, the resulting normalized event extents can be usedwith the actual event extents (e.g., production rates) to allocate theactual event extent (e.g., production rates) across the one or moreevent (e.g., fluid) inflow locations along the length being monitored(e.g., within the wellbore). Thus, method 500 of FIG. 5 can furthercomprise optional step 510 of allocating the event extent across the oneor more locations. As an example, for events comprising fluid inflow andevent extents comprising fluid inflow rates at one or more locations, ifthe models indicate that thirty percent of a liquid phase inflow rate isoccurring at a first location and seventy percent is occurring at asecond location, the actual production rate can be allocated so thatthirty percent of the produced liquid phase flowrate is attributed tothe first location and the remaining seventy percent of the liquid phaseflow rate is flowing into the wellbore at the second location. Theallocations can be made for one or more of the fluid inflow rates and/orfluid phase inflow rates, where the actual production rates for thefluids and/or fluid phases can be used with the corresponding modeloutputs for one or more fluids and/or fluid phases. The allocationprocess can allow for an improved accuracy for the determination offluid inflow rates at the one or more locations along the wellbore.

Any of the systems and methods disclosed herein can be carried out on acomputer or other device comprising a processor (e.g., a desktopcomputer, a laptop computer, a tablet, a server, a smartphone, or somecombination thereof), such as the acquisition device 160 of FIG. 1 .FIG. 6 illustrates a computer system 680 suitable for implementing oneor more embodiments disclosed herein such as the acquisition device orany portion thereof. The computer system 680 includes a processor 682(which may be referred to as a central processor unit or CPU) that is incommunication with memory devices including secondary storage 684, readonly memory (ROM) 686, random access memory (RAM) 688, input/output(I/O) devices 690, and network connectivity devices 692. The processor682 may be implemented as one or more CPU chips.

It is understood that by programming and/or loading executableinstructions onto the computer system 680, at least one of the CPU 682,the RAM 688, and the ROM 686 are changed, transforming the computersystem 680 in part into a particular machine or apparatus having thenovel functionality taught by the present disclosure. It is fundamentalto the electrical engineering and software engineering arts thatfunctionality that can be implemented by loading executable softwareinto a computer can be converted to a hardware implementation bywell-known design rules. Decisions between implementing a concept insoftware versus hardware typically hinge on considerations of stabilityof the design and numbers of units to be produced rather than any issuesinvolved in translating from the software domain to the hardware domain.Generally, a design that is still subject to frequent change may bepreferred to be implemented in software, because re-spinning a hardwareimplementation is more expensive than re-spinning a software design.Generally, a design that is stable that will be produced in large volumemay be preferred to be implemented in hardware, for example in anapplication specific integrated circuit (ASIC), because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

Additionally, after the system 680 is turned on or booted, the CPU 682may execute a computer program or application. For example, the CPU 682may execute software or firmware stored in the ROM 686 or stored in theRAM 688. In some cases, on boot and/or when the application isinitiated, the CPU 682 may copy the application or portions of theapplication from the secondary storage 684 to the RAM 688 or to memoryspace within the CPU 682 itself, and the CPU 682 may then executeinstructions of which the application is comprised. In some cases, theCPU 682 may copy the application or portions of the application frommemory accessed via the network connectivity devices 692 or via the I/Odevices 690 to the RAM 688 or to memory space within the CPU 682, andthe CPU 682 may then execute instructions of which the application iscomprised. During execution, an application may load instructions intothe CPU 682, for example load some of the instructions of theapplication into a cache of the CPU 682. In some contexts, anapplication that is executed may be said to configure the CPU 682 to dosomething, e.g., to configure the CPU 682 to perform the function orfunctions promoted by the subject application. When the CPU 682 isconfigured in this way by the application, the CPU 682 becomes aspecific purpose computer or a specific purpose machine.

The secondary storage 684 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 688 is not large enough tohold all working data. Secondary storage 684 may be used to storeprograms which are loaded into RAM 688 when such programs are selectedfor execution. The ROM 686 is used to store instructions and perhapsdata which are read during program execution. ROM 686 is a non-volatilememory device which typically has a small memory capacity relative tothe larger memory capacity of secondary storage 684. The RAM 688 is usedto store volatile data and perhaps to store instructions. Access to bothROM 686 and RAM 688 is typically faster than to secondary storage 684.The secondary storage 684, the RAM 688, and/or the ROM 686 may bereferred to in some contexts as computer readable storage media and/ornon-transitory computer readable media.

I/O devices 690 may include printers, video monitors, electronicdisplays (e.g., liquid crystal displays (LCDs), plasma displays, organiclight emitting diode displays (OLED), touch sensitive displays, etc.),keyboards, keypads, switches, dials, mice, track balls, voicerecognizers, card readers, paper tape readers, or other well-known inputdevices.

The network connectivity devices 692 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fiber distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 692 may enable the processor 682 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 682 mightreceive information from the network, or might output information to thenetwork (e.g., to an event database) in the course of performing theabove-described method steps. Such information, which is oftenrepresented as a sequence of instructions to be executed using processor682, may be received from and outputted to the network, for example, inthe form of a computer data signal embodied in a carrier wave.

Such information, which may include data or instructions to be executedusing processor 682 for example, may be received from and outputted tothe network, for example, in the form of a computer data baseband signalor signal embodied in a carrier wave. The baseband signal or signalembedded in the carrier wave, or other types of signals currently usedor hereafter developed, may be generated according to several knownmethods. The baseband signal and/or signal embedded in the carrier wavemay be referred to in some contexts as a transitory signal.

The processor 682 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 684), flash drive, ROM 686, RAM 688, or the network connectivitydevices 692. While only one processor 682 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors. Instructions,codes, computer programs, scripts, and/or data that may be accessed fromthe secondary storage 684, for example, hard drives, floppy disks,optical disks, and/or other device, the ROM 686, and/or the RAM 688 maybe referred to in some contexts as non-transitory instructions and/ornon-transitory information.

In an embodiment, the computer system 680 may comprise two or morecomputers in communication with each other that collaborate to perform atask. For example, but not by way of limitation, an application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of the instructions of the application. Alternatively, thedata processed by the application may be partitioned in such a way as topermit concurrent and/or parallel processing of different portions of adata set by the two or more computers. In an embodiment, virtualizationsoftware may be employed by the computer system 680 to provide thefunctionality of a number of servers that is not directly bound to thenumber of computers in the computer system 680. For example,virtualization software may provide twenty virtual servers on fourphysical computers. In an embodiment, the functionality disclosed abovemay be provided by executing the application and/or applications in acloud computing environment. Cloud computing may comprise providingcomputing services via a network connection using dynamically scalablecomputing resources. Cloud computing may be supported, at least in part,by virtualization software. A cloud computing environment may beestablished by an enterprise and/or may be hired on an as-needed basisfrom a third party provider. Some cloud computing environments maycomprise cloud computing resources owned and operated by the enterpriseas well as cloud computing resources hired and/or leased from a thirdparty provider.

In an embodiment, some or all of the functionality disclosed above maybe provided as a computer program product. The computer program productmay comprise one or more computer readable storage medium havingcomputer usable program code embodied therein to implement thefunctionality disclosed above. The computer program product may comprisedata structures, executable instructions, and other computer usableprogram code. The computer program product may be embodied in removablecomputer storage media and/or non-removable computer storage media. Theremovable computer readable storage medium may comprise, withoutlimitation, a paper tape, a magnetic tape, magnetic disk, an opticaldisk, a solid state memory chip, for example analog magnetic tape,compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,digital cards, multimedia cards, and others. The computer programproduct may be suitable for loading, by the computer system 680, atleast portions of the contents of the computer program product to thesecondary storage 684, to the ROM 686, to the RAM 688, and/or to othernon-volatile memory and volatile memory of the computer system 680. Theprocessor 682 may process the executable instructions and/or datastructures in part by directly accessing the computer program product,for example by reading from a CD-ROM disk inserted into a disk driveperipheral of the computer system 680. Alternatively, the processor 682may process the executable instructions and/or data structures byremotely accessing the computer program product, for example bydownloading the executable instructions and/or data structures from aremote server through the network connectivity devices 692. The computerprogram product may comprise instructions that promote the loadingand/or copying of data, data structures, files, and/or executableinstructions to the secondary storage 684, to the ROM 686, to the RAM688, and/or to other non-volatile memory and volatile memory of thecomputer system 680.

In some contexts, the secondary storage 684, the ROM 686, and the RAM688 may be referred to as a non-transitory computer readable medium or acomputer readable storage media. A dynamic RAM embodiment of the RAM688, likewise, may be referred to as a non-transitory computer readablemedium in that while the dynamic RAM receives electrical power and isoperated in accordance with its design, for example during a period oftime during which the computer system 680 is turned on and operational,the dynamic RAM stores information that is written to it. Similarly, theprocessor 682 may comprise an internal RAM, an internal ROM, a cachememory, and/or other internal non-transitory storage blocks, sections,or components that may be referred to in some contexts as non-transitorycomputer readable media or computer readable storage media.

Having described various systems and methods, certain embodiments caninclude, but are not limited to:

In a first embodiment, a method of determining a presence or extent ofan event comprises: determining a plurality of temperature features froma temperature sensing signal; determining one or more frequency domainfeatures from an acoustic signal; and using at least one temperaturefeature of the plurality of temperature features and at least onefrequency domain feature of the one or more frequency domain features todetermine a presence or extent of the event at one or more locations.

A second embodiment can include the method of the first embodiment,wherein the one or more events comprise one or more wellbore events, andwherein the one or more wellbore events comprise one or more of: a fluidinflow, a fluid outflow, a fluid phase segregation, a fluid flowdiscrimination within a conduit, a well integrity monitoring, an in-wellleak detection, an annular fluid flow, an overburden monitoring, a fluidflow detection behind a casing, a fluid induced hydraulic fracturedetection in an overburden, a sand ingress, a wax deposition, or a sandflow along a wellbore.

A third embodiment can include the method of the first or secondembodiment, wherein the one or more events comprise one or more securityevents, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoringevents, or dam monitoring events.

A fourth embodiment can include the method of any one of the first tothird embodiments, wherein the plurality of temperature featurescomprises a depth derivative of temperature with respect to depth.

A fifth embodiment can include the method of any one of the first tofourth embodiments, wherein the plurality of temperature featurescomprises a temperature excursion measurement, wherein the temperatureexcursion measurement comprises a difference between a temperaturereading at a first depth and a smoothed temperature reading over a depthrange, wherein the first depth is within the depth range.

A sixth embodiment can include the method of any one of the first tofifth embodiments, wherein the plurality of temperature featurescomprises a baseline temperature excursion, wherein the baselinetemperature excursion comprises a derivative of a baseline excursionwith depth, wherein the baseline excursion comprises a differencebetween a baseline temperature profile and a smoothed temperatureprofile.

A seventh embodiment can include the method of any one of the first tosixth embodiments, wherein the plurality of temperature featurescomprises a peak-to-peak value, wherein the peak-to-peak value comprisesa derivative of a peak-to-peak difference with depth, wherein thepeak-to-peak difference comprises a difference between a peak hightemperature reading and a peak low temperature reading with an interval.

An eighth embodiment can include the method of any one of the first toseventh embodiments, wherein the plurality of temperature featurescomprises an autocorrelation, wherein the autocorrelation is across-correlation of the temperature sensing signal with itself.

A ninth embodiment can include the method of any one of the first toeighth embodiments, wherein the plurality of temperature featurescomprises a Fast Fourier Transform (FFT) of the temperature sensingsignal.

A tenth embodiment can include the method of any one of the first toninth embodiments, wherein the plurality of temperature featurescomprises a Laplace transform of the temperature sensing signal.

An eleventh embodiment can include the method of any one of the first totenth embodiments, wherein the plurality of temperature featurescomprises a wavelet transform of the temperature sensing signal or awavelet transform of the derivative of the temperature sensing signalwith length (e.g., depth).

A twelfth embodiment can include the method of the eleventh embodiment,wherein the wavelet comprises a Morse wavelet, an analytical wavelet, aBump wavelet, or a combination thereof.

A thirteenth embodiment can include the method of any one of the firstto twelfth embodiments, wherein the plurality of temperature featurescomprises a derivative of flowing temperature with respect to depth.

A fourteenth embodiment can include the method of any one of the firstto thirteenth embodiments, wherein the plurality of temperature featurescomprises a heat loss parameter.

A fifteenth embodiment can include the method of any one of the first tofourteenth embodiments, wherein the plurality of temperature featurescomprise a time-depth derivative, a depth-time derivative, or both.

A sixteenth embodiment can include the method of any one of the first tofifteenth embodiments, wherein the one or more frequency domain featurescomprise at least one of: a spectral centroid, a spectral spread, aspectral roll-off, a spectral skewness, an RMS band energy, a total RMSenergy, a spectral flatness, a spectral slope, a spectral kurtosis, aspectral flux, or a spectral autocorrelation function.

A seventeenth embodiment can include the method of any one of the firstto sixteenth embodiments, wherein using the at least one temperaturefeature and the at least one frequency domain feature comprises: usingthe at least one temperature feature in a first model; using the atleast one frequency domain feature of the one or more frequency domainfeatures in a second model; combining an output from the first model andan output from the second model to form a combined output; anddetermining a presence or extent of the event based on the combinedoutput.

An eighteenth embodiment can include the method of the seventeenthembodiment, wherein the first model comprise one or more multivariatemodels, and wherein the output from each multivariate model of the oneor more multivariate model comprises an indication of the presence ofthe event at one or more locations.

A nineteenth embodiment can include the method of the eighteenthembodiment, wherein the second model comprises a regression model, andwherein the output from the regression model comprises an indication ofthe presence or extent of the event at the one or more locations.

A twentieth embodiment can include the method of the nineteenthembodiment, wherein combining the output from the first model with theoutput from the second model comprises determining the combined outputas a function of: 1) the output from the first model, and 2) the outputfrom the second model.

A twenty first embodiment can include the method of any one of the firstto twentieth embodiments, further comprising: receiving an independentindication of extent of the event; and allocating a portion of the eventextent to the one or more locations based on the event extent at the oneor more locations based on the combined output.

A twenty second embodiment can include the method of any one of thefirst to twenty first embodiments, further comprising: receiving thetemperature sensing signal from a sensor comprising a fiber optic basedtemperature sensor or receiving the acoustic signal from a sensorcomprising a fiber optic based acoustic sensor.

A twenty third embodiment can include the method of the twenty secondembodiment, wherein the fiber optic based temperature sensor or thefiber optic based acoustic sensor is disposed in a wellbore.

A twenty fourth embodiment can include the method of any one of thefirst to twenty third embodiments, further comprising: denoising andcalibrating the temperature sensing signal prior to determining the oneor more temperature features; or normalizing the one or more temperaturefeatures prior to determining the presence of the one or more events.

In a twenty fifth embodiment, a method of determining a presence orextent of an event comprises:

determining a plurality of temperature features from a temperaturesensing signal, wherein the plurality of temperature features compriseat least two of: a depth derivative of temperature with respect todepth, a temperature excursion measurement, a baseline temperatureexcursion, or a peak-to-peak value, an autocorrelation, a Fast FourierTransform (FFT) of the temperature sensing signal, a Laplace transformof the temperature sensing signal, a wavelet transform of thetemperature sensing signal or of a derivative of the temperature sensingsignal with respect to length (e.g., depth), or a derivative of flowingtemperature with respect to length (depth), as described by Equation(1), a heat loss parameter, a time-depth derivative, or a depth-timederivative; determining one or more frequency domain features from anacoustic signal originated in the wellbore; and using at least onetemperature feature of the plurality of temperature features and atleast one frequency domain feature of the one or more frequency domainfeatures to determine the presence or extent of the event at one or morelocations.

A twenty sixth embodiment can include the method of the twenty fifthembodiment, wherein the one or more events comprise one or more wellboreevents, and wherein the one or more wellbore events comprise one or moreof: a fluid inflow, a fluid outflow, a fluid phase segregation, a fluidflow discrimination within a conduit, a well integrity monitoring, anin-well leak detection, an annular fluid flow, an overburden monitoring,a fluid flow detection behind a casing, a sand ingress, a waxdeposition, or a sand flow along a wellbore.

A twenty seventh embodiment can include the method of the twenty fifthembodiment, wherein the one or more events comprise one or more securityevents, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoringevents, or dam monitoring events.

A twenty eighth embodiment can include the method of the twenty fifthembodiment, wherein the one or more events comprises a fluid inflow atone or more locations.

A twenty ninth embodiment can include the method of the twenty eighthembodiment, wherein the fluid inflow is a liquid inflow at the one ormore locations.

A thirtieth embodiment can include the method of the twenty ninthembodiment, wherein the liquid inflow comprises an aqueous liquid, ahydrocarbon liquid, or a combination of both an aqueous liquid and ahydrocarbon liquid.

A thirty first embodiment can include the method of any one of thetwenty fifth to thirtieth embodiments, wherein the temperature excursionmeasurement comprises a difference between a temperature reading at afirst depth and a smoothed temperature reading over a depth range,wherein the first depth is within the depth range.

A thirty second embodiment can include the method of any one of thetwenty fifth to thirty first embodiments, wherein the baselinetemperature excursion comprises a derivative of a baseline excursionwith depth, wherein the baseline excursion comprises a differencebetween a baseline temperature profile and a smoothed temperatureprofile.

A thirty third embodiment can include the method of any one of thetwenty fifth to thirty second embodiments, wherein the peak-to-peakvalue comprises a derivative of a peak-to-peak difference with depth,wherein the peak-to-peak difference comprises a difference between apeak high temperature reading and a peak low temperature reading with aninterval.

A thirty fourth embodiment can include the method of any one of thetwenty fifth to thirty third embodiments, wherein the one or morefrequency domain features comprise at least one of: a spectral centroid,a spectral spread, a spectral roll-off, a spectral skewness, an RMS bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, or a spectral autocorrelationfunction.

A thirty fifth embodiment can include the method of any one of thetwenty fifth to thirty fourth embodiments, wherein using the at leastone temperature feature and the at least one frequency domain featurecomprises: using the at least one temperature features in a first model;using at least one frequency domain feature of the one or more frequencydomain features in a second model; combining an output from the firstmodel and an output from the second model to form a combined output; anddetermining a presence or extent of the event at the one or morelocations based on the combined output.

A thirty sixth embodiment can include the method of the thirty fifthembodiment, wherein the first model comprise one or more multivariatemodels, and wherein the output from each multivariate model of the oneor more multivariate model comprises an indication of the presence orabsence of the event at one or more locations along the wellbore.

A thirty seventh embodiment can include the method of the thirty fifthembodiment, wherein the second model comprises a regression model, andwherein the output from the regression model comprises an indication ofa presence or an extent thereof at the one or more locations.

A thirty eighth embodiment can include the method of the thirty fifthembodiment, wherein combining the output from the first model with theoutput from the second model comprises determining the combined outputas a function of: 1) the output from the first model, and 2) the outputfrom the second model.

A thirty ninth embodiment can include the method of any one of thetwenty fifth to thirty eighth embodiments, further comprising: receivingan independent indication of an event extent; and allocating a portionof the event extent to the one or more locations based on the determinedevent extent at the one or more locations based on the combined output.

In a fortieth embodiment, a system of determining a presence or extentof an event comprises:

a processor; a memory; and an analysis program stored in the memory,wherein the analysis program is configured, when executed on theprocessor, to: receive a temperature sensing signal and an acousticsignal; determine a plurality of temperature features from thetemperature sensing signal; determine one or more frequency domainfeatures from the acoustics signal; and determine a presence or extentof the event at one or more locations using at least one temperaturefeature of the plurality of temperature features and at least onefrequency domain feature of the one or more frequency domain features.

A forty first embodiment can include the system of the fortiethembodiment, wherein the analysis program is further configured to: usethe at least one temperature features in a first model; use at least onefrequency domain feature of the one or more frequency domain features ina second model; combine an output from the first model and an outputfrom the second model to form a combined output; and determine apresence or extent of the event at the one or more locations based onthe combined output.

A forty second embodiment can include the system of the forty firstembodiment, wherein the first model comprises one or more multivariatemodels, and wherein the output from each multivariate model of the oneor more multivariate model comprises an indication of the one or morelocations.

A forty third embodiment can include the system of any one of thefortieth to forty second embodiments, wherein the second model comprisesa regression model, and wherein the output from the regression modelcomprises an indication an extent of the event at the one or morelocations.

A forty fourth embodiment can include the system of the forty firstembodiment, wherein the analysis program is further configured to:combine the output from the first model with the output from the secondmodel as a function of: 1) the output from the first model, and 2) theoutput from the second model.

A forty fifth embodiment can include the system of any one of thefortieth to forty fourth embodiments, wherein the analysis program isfurther configured to: receive an independent indication of an eventextent; and allocate a portion of the event extent to the one or morelocations based on the determined event extent at the one or morelocations based on the combined output.

A forty sixth embodiment can include the system of any one of thefortieth to forty fifth embodiments, wherein the plurality oftemperature features comprise at least two of: a depth derivative oftemperature with respect to depth, a temperature excursion measurement,a baseline temperature excursion, or a peak-to-peak value, anautocorrelation, a Fast Fourier Transform (FFT) of the temperaturesensing signal, a Laplace transform of the temperature sensing signal, awavelet transform of the temperature sensing signal or of a derivativeof the temperature sensing signal with respect to length (e.g., depth),or a derivative of flowing temperature with respect to length (depth),as described by Equation (1), a heat loss parameter, a time-depthderivative, or a depth-time derivative.

A forty seventh embodiment can include the system of any one of thefortieth to forty sixth embodiments, wherein the temperature excursionmeasurement comprises a difference between a temperature reading at afirst depth and a smoothed temperature reading over a depth range,wherein the first depth is within the depth range.

A forty eighth embodiment can include the system of any one of thefortieth to forty seventh embodiments, wherein the baseline temperatureexcursion comprises a derivative of a baseline excursion with depth,wherein the baseline excursion comprises a difference between a baselinetemperature profile and a smoothed temperature profile.

A forty ninth embodiment can include the system of any one of thefortieth to forty eighth embodiments, wherein the peak-to-peak valuecomprises a derivative of a peak-to-peak difference with depth, whereinthe peak-to-peak difference comprises a difference between a peak hightemperature reading and a peak low temperature reading with an interval.

A fiftieth embodiment can include the system of any one of the fortiethto forty ninth embodiments, wherein the one or more frequency domainfeatures comprise at least one of: a spectral centroid, a spectralspread, a spectral roll-off, a spectral skewness, an RMS band energy, atotal RMS energy, a spectral flatness, a spectral slope, a spectralkurtosis, a spectral flux, or a spectral autocorrelation function.

The embodiments disclosed herein have included systems and methods fordetecting and/or characterizing sand ingress and/or sand transportwithin a subterranean wellbore, or a plurality of such wellbores. Thus,through use of the systems and methods described herein, one may moreeffectively limit or avoid sand ingress and accumulation with a wellboreso as to enhance the economic production therefrom.

While exemplary embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thescope or teachings herein. The embodiments described herein areexemplary only and are not limiting. Many variations and modificationsof the systems, apparatus, and processes described herein are possibleand are within the scope of the disclosure. Accordingly, the scope ofprotection is not limited to the embodiments described herein, but isonly limited by the claims that follow, the scope of which shall includeall equivalents of the subject matter of the claims. Unless expresslystated otherwise, the steps in a method claim may be performed in anyorder. The recitation of identifiers such as (a), (b), (c) or (1), (2),(3) before steps in a method claim are not intended to and do notspecify a particular order to the steps, but rather are used to simplifysubsequent reference to such steps.

1. A method of determining a presence or extent of an event, the method comprising: determining a plurality of temperature features from a temperature sensing signal; determining one or more frequency domain features from an acoustic signal; and using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features to determine a presence or extent of the event at one or more locations.
 2. The method of claim 1, wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of: a fluid inflow, a fluid outflow, a fluid phase segregation, a fluid flow discrimination within a conduit, a well integrity monitoring, an in-well leak detection, an annular fluid flow, an overburden monitoring, a fluid flow detection behind a casing, a fluid induced hydraulic fracture detection in an overburden, a sand ingress, a wax deposition, or a sand flow along a wellbore.
 3. The method of claim 1, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂ injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events.
 4. The method of claim 1, wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth.
 5. The method of claim 1, wherein the plurality of temperature features comprises a temperature excursion measurement, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.
 6. The method of claim 1, wherein the plurality of temperature features comprises a baseline temperature excursion, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.
 7. The method of claim 1, wherein the plurality of temperature features comprises a peak-to-peak value, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.
 8. The method of claim 1, wherein the plurality of temperature features comprises an autocorrelation, wherein the autocorrelation is a cross-correlation of the temperature sensing signal with itself.
 9. The method of claim 1, wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal.
 10. The method of claim 1, wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal.
 11. The method of claim 1, wherein the plurality of temperature features comprises a wavelet transform of the temperature sensing signal or a wavelet transform of the derivative of the temperature sensing signal with length (e.g., depth).
 12. The method of claim 11, wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof.
 13. The method of claim 1, wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth.
 14. The method of claim 1, wherein the plurality of temperature features comprises a heat loss parameter.
 15. The method of claim 1, wherein the plurality of temperature features comprise a time-depth derivative, a depth-time derivative, or both.
 16. The method of claim 1, wherein the one or more frequency domain features comprise at least one of: a spectral centroid, a spectral spread, a spectral roll-off, a spectral skewness, an RMS band energy, a total RMS energy, a spectral flatness, a spectral slope, a spectral kurtosis, a spectral flux, or a spectral autocorrelation function.
 17. The method of claim 1, wherein using the at least one temperature feature and the at least one frequency domain feature comprises: using the at least one temperature feature in a first model; using the at least one frequency domain feature of the one or more frequency domain features in a second model; combining an output from the first model and an output from the second model to form a combined output; and determining a presence or extent of the event based on the combined output.
 18. The method of claim 17, wherein the first model comprise one or more multivariate models, and wherein the output from each multivariate model of the one or more multivariate model comprises an indication of the presence of the event at one or more locations.
 19. The method of claim 18, wherein the second model comprises a regression model, and wherein the output from the regression model comprises an indication of the presence or extent of the event at the one or more locations.
 20. The method of claim 19, wherein combining the output from the first model with the output from the second model comprises determining the combined output as a function of: 1) the output from the first model, and 2) the output from the second model.
 21. The method of claim 1, further comprising: receiving an independent indication of extent of the event; and allocating a portion of the event extent to the one or more locations based on the event extent at the one or more locations based on the combined output.
 22. The method of claim 1, further comprising: receiving the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor or receiving the acoustic signal from a sensor comprising a fiber optic based acoustic sensor.
 23. The method of claim 22, wherein the fiber optic based temperature sensor or the fiber optic based acoustic sensor is disposed in a wellbore.
 24. The method of claim 1, further comprising: denoising and calibrating the temperature sensing signal prior to determining the one or more temperature features; or normalizing the one or more temperature features prior to determining the presence of the one or more events.
 25. A method of determining a presence or extent of an event, the method comprising: determining a plurality of temperature features from a temperature sensing signal, wherein the plurality of temperature features comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, or a peak-to-peak value, an autocorrelation, a Fast Fourier Transform (FFT) of the temperature sensing signal, a Laplace transform of the temperature sensing signal, a wavelet transform of the temperature sensing signal or of a derivative of the temperature sensing signal with respect to length (e.g., depth), or a derivative of flowing temperature with respect to length (depth), as described by Equation (1), a heat loss parameter, a time-depth derivative, or a depth-time derivative; determining one or more frequency domain features from an acoustic signal originated in the wellbore; and using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features to determine the presence or extent of the event at one or more locations.
 26. The method of claim 25, wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of: a fluid inflow, a fluid outflow, a fluid phase segregation, a fluid flow discrimination within a conduit, a well integrity monitoring, an in-well leak detection, an annular fluid flow, an overburden monitoring, a fluid flow detection behind a casing, a sand ingress, a wax deposition, or a sand flow along a wellbore.
 27. The method of claim 25, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂ injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events.
 28. The method of claim 25, wherein the one or more events comprises a fluid inflow at one or more locations.
 29. The method of claim 28, wherein the fluid inflow is a liquid inflow at the one or more locations.
 30. The method of claim 29, wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both an aqueous liquid and a hydrocarbon liquid.
 31. The method of claim 25, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.
 32. The method of claim 25, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.
 33. The method of claim 25, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.
 34. The method of claim 25, wherein the one or more frequency domain features comprise at least one of: a spectral centroid, a spectral spread, a spectral roll-off, a spectral skewness, an RMS band energy, a total RMS energy, a spectral flatness, a spectral slope, a spectral kurtosis, a spectral flux, or a spectral autocorrelation function.
 35. The method of claim 25, wherein using the at least one temperature feature and the at least one frequency domain feature comprises: using the at least one temperature features in a first model; using at least one frequency domain feature of the one or more frequency domain features in a second model; combining an output from the first model and an output from the second model to form a combined output; and determining a presence or extent of the event at the one or more locations based on the combined output.
 36. The method of claim 35, wherein the first model comprise one or more multivariate models, and wherein the output from each multivariate model of the one or more multivariate model comprises an indication of the presence or absence of the event at one or more locations along the wellbore.
 37. The method of claim 35, wherein the second model comprises a regression model, and wherein the output from the regression model comprises an indication of a presence or an extent thereof at the one or more locations.
 38. The method of claim 35, wherein combining the output from the first model with the output from the second model comprises determining the combined output as a function of: 1) the output from the first model, and 2) the output from the second model.
 39. The method of claim 25, further comprising: receiving an independent indication of an event extent; and allocating a portion of the event extent to the one or more locations based on the determined event extent at the one or more locations based on the combined output.
 40. A system of determining a presence or extent of an event, the system comprising: a processor; a memory; and an analysis program stored in the memory, wherein the analysis program is configured, when executed on the processor, to: receive a temperature sensing signal and an acoustic signal; determine a plurality of temperature features from the temperature sensing signal; determine one or more frequency domain features from the acoustics signal; and determine a presence or extent of the event at one or more locations using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features.
 41. The system of claim 40, wherein the analysis program is further configured to: use the at least one temperature features in a first model; use at least one frequency domain feature of the one or more frequency domain features in a second model; combine an output from the first model and an output from the second model to form a combined output; and determine a presence or extent of the event at the one or more locations based on the combined output.
 42. The system of claim 41, wherein the first model comprises one or more multivariate models, and wherein the output from each multivariate model of the one or more multivariate model comprises an indication of the one or more locations.
 43. The system of claim 40, wherein the second model comprises a regression model, and wherein the output from the regression model comprises an indication an extent of the event at the one or more locations.
 44. The system of claim 41, wherein the analysis program is further configured to: combine the output from the first model with the output from the second model as a function of: 1) the output from the first model, and 2) the output from the second model.
 45. The system of claim 40, wherein the analysis program is further configured to: receive an independent indication of an event extent; and allocate a portion of the event extent to the one or more locations based on the determined event extent at the one or more locations based on the combined output.
 46. The system of claim 40, wherein the plurality of temperature features comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, or a peak-to-peak value, an autocorrelation, a Fast Fourier Transform (FFT) of the temperature sensing signal, a Laplace transform of the temperature sensing signal, a wavelet transform of the temperature sensing signal or of a derivative of the temperature sensing signal with respect to length (e.g., depth), or a derivative of flowing temperature with respect to length (depth), as described by Equation (1), a heat loss parameter, a time-depth derivative, or a depth-time derivative.
 47. The system of claim 40, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.
 48. The system of claim 40, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.
 49. The system of claim 40, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.
 50. The system of claim 40, wherein the one or more frequency domain features comprise at least one of: a spectral centroid, a spectral spread, a spectral roll-off, a spectral skewness, an RMS band energy, a total RMS energy, a spectral flatness, a spectral slope, a spectral kurtosis, a spectral flux, or a spectral autocorrelation function. 